achuthasubhash

Complete-Life-Cycle-of-a-Data-Science-Project

# Complete-Life-Cycle-of-a-Data-Science-Project ***CREDITS:All corresponding resources*** ***MOTIVATION:Motivation to create this repository to help upcoming aspirants and help to others in the data science field*** https://www.theinsaneapp.com/2021/03/how-to-build-machine-learning-project.html **** If you like my work. please buy me a coffee it motivate me -> https://www.buymeacoffee.com/achuthasubhash?new=1 **** ***Business understanding*** ***1.Data collection*** Data consists of 3 kinds a.Structure data (tabular data,etc...) b.Unstructured data (images,text,audio,etc...) c.semi structured data (XML,JSON,etc...) variable a.qualitative (nominal,ordinal,binary) b.quantitative(discrete,continuous) https://www.chi2innovations.com/blog/discover-data-blog-series/data-types-101/ database scraping data from websites purchasing data data from surveys data, sensors, cameras, apis etc. cleanlab https://l7.curtisnorthcutt.com/cleanlab-python-package https://github.com/cgnorthcutt/cleanlab https://github.com/cgnorthcutt/label-errors https://github.com/cgnorthcutt/rankpruning https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise Measure Data Quality ydata-quality https://github.com/ydataai/ydata-synthetic https://towardsdatascience.com/how-can-i-measure-data-quality-9d31acfeb969 a.Web scraping best article to refer-https://towardsdatascience.com/choose-the-best-python-web-scraping-library-for-your-application-91a68bc81c4f https://www.analyticsvidhya.com/blog/2019/10/web-scraping-hands-on-introduction-python/?utm_source=linkedin&utm_medium=KJ|link|weekend-blogs|blogs|44087|0.875 https://www.analyticsvidhya.com/blog/2019/10/web-scraping-hands-on-introduction-python/?utm_source=linkedin&utm_medium=AV|link|high-performance-blog|blogs|44204|0.375 https://www.kdnuggets.com/2021/02/6-web-scraping-tools.html https://www.bigdatanews.datasciencecentral.com/profiles/blogs/top-30-free-web-scraping-software https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d https://medium.com/analytics-vidhya/master-web-scraping-completly-from-zero-to-hero-38051423256b 1.Beautifulsoup https://www.freecodecamp.org/news/how-to-scrape-websites-with-python-and-beautifulsoup-5946935d93fe/ mechanicalsoup https://analyticsindiamag.com/mechanicalsoup-web-scraping-custom-dataset-tutorial/ 2.Scrapy,PyScrappy,Pandas Datareader,Instaloader,lxml 3.Selenium https://www.freecodecamp.org/news/better-web-scraping-in-python-with-selenium-beautiful-soup-and-pandas-d6390592e251/ 4.Request to access data 5.AUTOSCRAPER - https://github.com/alirezamika/autoscraper https://www.youtube.com/watch?v=9BQ353Yu1D0 https://www.analyticsvidhya.com/blog/2021/04/automate-web-scraping-using-python-autoscraper-library/ scrapeasy Scrape Any Website in Seconds with One Line of Code https://github.com/joelbarmettlerUZH/Scrapeasy Scrap Images From E-Commerce Website Using AutoScraper https://www.analyticsvidhya.com/blog/2021/05/scrap-images-from-e-commerce-website-using-autoscraper-library/ amazon auto scraper library https://webautomation.io/ Listly https://www.listly.io/r/stdfr FiftyOne Now easier to download and evaluate https://towardsdatascience.com/googles-open-images-now-easier-to-download-and-evaluate-with-fiftyone-615ce0482c02 webbot https://pypi.org/project/webbot/ gazpacho https://github.com/maxhumber/gazpacho html_scraper_streamlit_app https://www.youtube.com/watch?v=6U5xJ3mXRKA&feature=youtu.be 6.Twitter scraping tool (𝚝𝚠𝚒𝚗𝚝 or tweepy or tweetlib)-https://github.com/twintproject/twint twitterscraper https://www.youtube.com/watch?v=MpIi4HtCiVk twython https://github.com/ryanmcgrath/twython twarc https://github.com/DocNow/twarc https://scholarslab.github.io/learn-twarc/01-quick-start.html snscrape extract twitterr data https://github.com/JustAnotherArchivist/snscrape Scweet A simple and unlimited twitter scraper https://github.com/Altimis/Scweet GetOldTweets3,GoogleNews,snscrape,GetOldTweets3 Scrape Twitter for Tweets https://github.com/taspinar/twitterscraper HAR File Web Scraper https://stevesie.com/har-file-web-scraper https://www.youtube.com/watch?v=LcqVDfueb8g https://analyticsindiamag.com/complete-tutorial-on-twint-twitter-scraping-without-twitters-api/ https://developer.twitter.com/en/docs pytrends https://medium.com/nerd-for-tech/scraping-data-from-online-platforms-to-enhance-time-series-forecasts-6eec3c68636d Scraping Instagram -instaloader https://thecleverprogrammer.com/2020/07/30/scraping-instagram-with-python/ Instascrape Scrape LinkedIn Profiles with ProxyCurl API Reddit Dataset Using PSAW and PRAW in Python Scraping Reddit using Python Reddit API Wrapper (PRAW) Scrape Wikipedia wikipedia https://www.thepythoncode.com/article/access-wikipedia-python patang - Scrape Product details from eCommerce Sites with Puppeteer and DOM String https://www.youtube.com/watch?v=3sgxRmyOuXs Download Wikipedia https://www.wikidata.org/wiki/Wikidata:Main_Page https://www.youtube.com/watch?v=hC1rY4lRY0s https://towardsdatascience.com/an-efficient-way-to-read-data-from-the-web-directly-into-python-a526a0b4f4cb Web Scraping to Create a CSV File https://thecleverprogrammer.com/2020/08/08/web-scraping-to-create-csv/ Amazon Web Scraper, Amazon Auto Scraper 7.urllib 8.pattern 9.Octoparse Easy Web Scraping https://www.octoparse.com/ prowebscraper https://prowebscraper.com/features Web scraper https://chrome.google.com/webstore/detail/web-scraper-free-web-scra/jnhgnonknehpejjnehehllkliplmbmhn?hl=en ParseHub https://www.parsehub.com/ https://analyticsindiamag.com/parsehub-no-code-gui-based-web-scraping-tool/ PyScrappy https://github.com/mldsveda/PyScrappy https://www.analyticsvidhya.com/blog/2022/02/web-scraping-with-pyscrappy/ Gazpacho https://github.com/maxhumber/gazpacho ScrapeSimple Website: https://www.scrapesimple.com Content Grabber https://contentgrabber.com/Manual/understanding_the_concept.htm Crawly https://crawly.diffbot.com/ Apify https://apify.com/ Mozenda Website: https://www.mozenda.com/ obsei https://github.com/lalitpagaria/obsei Diffbot https://analyticsindiamag.com/diffbot/ Trustpilot,webhose,scrapingbot lxml https://lxml.de/index.html#introduction ScrapingBee https://analyticsindiamag.com/scrapingbee-api/ Scrape HTML tables https://www.youtube.com/watch?v=6U5xJ3mXRKA&feature=youtu.be or pd.read_html requests-html https://github.com/kennethreitz/requests-html newspaper https://github.com/codelucas/newspaper https://www.youtube.com/watch?v=Hfry5XnISyc newspaper3k: https://newspaper.readthedocs.io # easily extract text from articles newscatcher https://github.com/kotartemiy/newscatcher https://www.youtube.com/watch?v=pHzOuizZq4I patang (extract product details) https://github.com/tejazz/patang lisc https://github.com/lisc-tools/lisc Helena WEB AUTOMATION FOR END USERS https://helena-lang.org/ pandas(read_html) wget,curl,parsehub,webhouse,octoparse,scraping bot,scraping bee,Common,Content Grabber,Docparser,Scraper API,Import.io,Altair Monarch,WebAutomation.io,WebScraper.io,Scrape.do, AvesAPI, ParseHub, Import.io, Octoparse, Scrapingdog, Diffbot, ScrapingBee, Grepsr, Scraper API, Scrapy Crawl Crawly https://crawly.diffbot.com/ HTML basics for web scraping,Web Scraping with Octoparse,Web Scraping with Selenium 10-best-web-scraping-tools https://www.scraperapi.com/blog/the-10-best-web-scraping-tools/ https://www.kdnuggets.com/2021/02/6-web-scraping-tools.html https://analyticsindiamag.com/complete-learning-path-to-web-scraping-with-all-major-tools/ https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d https://www.kdnuggets.com/2018/02/web-scraping-tutorial-python.html https://www.octoparse.com/ https://github.com/tirthajyoti/pydbgen https://www.mozenda.com/ https://www.mockaroo.com/ https://lionbridge.ai/ https://www.mturk.com/ https://appen.com/ 11.GoogleImageCrawler,google_images_download,bing_image https://www.freepik.com/popular-photos , https://stocksnap.io/ , https://www.pexels.com/ ,https://unsplash.com/ , https://pixabay.com/ b.Web Crawling https://python.libhunt.com/scrapy-alternatives Flat Data https://octo.github.com/projects/flat-data b.3rd party API'S 22 APIs every data scientist should learn https://www.springboard.com/library/data-science/top-apis-for-data-scientists/ c.creating own data (manual collection eg:google docx,servey,etc...) primary data d.etl awesome ETL https://github.com/pawl/awesome-etl#python https://github.com/achuthasubhash/awesome-etl 38x faster data pipelines with tf.data d.Databases Databases are 2 kind sequel and no sequel database sql,sql lite,mysql,mongodb,montydb,hadoop,elastic search,cassendra,amazon s3,hive,googlebigtable,AWS DynamoDB,HBase,oracle db sql https://mode.com/sql-tutorial/ https://www.w3schools.com/sql/ sql in python https://medium.com/jbennetcodes/how-to-rewrite-your-sql-queries-in-pandas-and-more-149d341fc53e PyMongo https://analyticsindiamag.com/guide-to-pymongo-a-python-wrapper-for-mongodb/ Cloud AI Data labeling service https://cloud.google.com/ai-platform/data-labeling/docs?utm_source=youtube&utm_medium=Unpaidsocial&utm_campaign=guo-20200503-Data-Labeling e.Online resources - ultimate resource https://datasetsearch.research.google.com/ https://medium.com/swlh/where-to-find-awesome-machine-learning-datasets-6bb909a3f350 10 BEST DATA COLLECTION TOOLS FOR EFFECTIVE RESULTS https://www.analyticsinsight.net/10-best-data-collection-tools-for-effective-results/ https://www.freecodecamp.org/news/https-medium-freecodecamp-org-best-free-open-data-sources-anyone-can-use-a65b514b0f2d/ https://research.google/tools/datasets/ Machine learning datasets https://www.datasetlist.com/ https://wiki.pathmind.com/open-datasets https://guides.library.cmu.edu/az.php https://docs.microsoft.com/en-us/azure/azure-sql/public-data-sets https://registry.opendata.aws/ https://paperswithcode.com/datasets https://datasets.quantumstat.com/ https://www.quandl.com/ http://dataportals.org/ https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findex https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public https://www.reddit.com/r/datasets/ https://ourworldindata.org/ https://data.worldbank.org/ https://data.world/ https://data.census.gov/cedsci/ https://data.seattle.gov/ https://www.openml.org/ https://visualdata.io/discovery World’s Largest Data Platform https://worlddata.ai/ Awesome list of datasets in 100+ categories https://www.kdnuggets.com/2021/05/awesome-list-datasets.html https://sebastianraschka.com/blog/2021/ml-dl-datasets.html https://enoumen.com/2021/04/23/data-sciences-datasets-data-visualization-data-analytics-big-data-data-lakes/ https://serokell.io/blog/best-machine-learning-datasets https://medium.com/@ODSC/25-excellent-machine-learning-open-datasets-940ca2124dfc 1)kaggle-https://www.kaggle.com/datasets , 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔𝚊𝚐𝚐𝚕𝚎𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜 Downloading Kaggle datasets directly into Google Colab -https://towardsdatascience.com/downloading-kaggle-datasets-directly-into-google-colab-c8f0f407d73a How to Download Kaggle Datasets using Jupyter Notebook https://www.analyticsvidhya.com/blog/2021/04/how-to-download-kaggle-datasets-using-jupyter-notebook/ 2)https://sebastianraschka.com/blog/2021/ml-dl-datasets.html movielens-https://grouplens.org/datasets/movielens/latest/ dagshub datset https://dagshub.com/explore/datasets 100+ of the Best Free Data Sources For Your Next Project https://www.columnfivemedia.com/100-best-free-data-sources-infographic/ World and national data, maps & rankings https://knoema.com/atlas/sources 3)data.gov-https://data.gov.in/ 4)uci-https://archive.ics.uci.edu/ml/datasets.php https://github.com/tirthajyoti/UCI-ML-API 5)Group Lens dataset https://grouplens.org/ Wikipedia ML Datasets https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research AWS Open Data Registry,data.gov (portals),YELP Open dataset,UNICEF Dataset,Big Bad NLP Database,Microsoft Dataset 6)world3bank https://data.world/ , worldbank 7)Google Cloud BigQuery public datasets Google Public Datasets-cloud.google.com/bigquery/public-data/ Google Cloud Data Catalog https://cloud.google.com/data-catalog Academic Torrents-https://academictorrents.com/check.htm?returnto=%2Fbrowse.php 8)online hacktons Datasets https://www.paperswithcode.com/datasets 9)image data from google_images_download https://www.visualdata.io/discovery http://xviewdataset.org/#dataset https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html 10)image data from Bing_Search image data from simple_image_download https://github.com/RiddlerQ/simple_image_download 11)https://www.columnfivemedia.com/100-best-free-data-sources-infographic graviti Unleash the Power of Unstructured Data https://www.graviti.com/?utm_medium=0730Ismael 12)Reddit:https://lnkd.in/dv5UCD4 https://www.reddit.com/r/datasets/ praw.Reddit https://github.com/praw-dev/praw 13)https://datasets.bifrost.ai/?ref=producthunt 14)data.world:https://lnkd.in/gEK897K 15)https://data.world/datasets/open-data https://tinyletter.com/data-is-plural 16)FiveThirtyEight :- https://lnkd.in/gyh-HDj , https://data.fivethirtyeight.com/ 17)BuzzFeed :- https://lnkd.in/gzPWyHj Buzzfeed News -github.com/BuzzFeedNews Socrata - https://opendata.socrata.com/ 18)Google public datasets :- https://lnkd.in/g5dH8qE Statistics Canada https://www.statcan.gc.ca/eng/start https://towardsdatascience.com/how-to-collect-data-from-statistics-canada-using-python-db8a81ce6475 Deep Image Search AI-based image search engine https://github.com/TechyNilesh/DeepImageSearch https://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free 19)Quandl :- https://www.quandl.com stock data statista : https://www.statista.com/ stock data 20)socorateopendata :- https://lnkd.in/gea7JMz 21)AcedemicTorrents :- https://lnkd.in/g-Ur9Xy 22) Automates Image Annotation for Deep Learning Models https://medium.com/towards-artificial-intelligence/improving-data-labeling-efficiency-with-auto-labeling-uncertainty-estimates-and-active-learning-5848272365be Label Studio,Sloth,LabelBox,TagTog,Amazon SageMaker GroundTruth,Playment,Superannotate,Playment,Dataturk,LightTag,Superannotate,CVAT,sloth,LabelImg,cvat Automate data preparation https://www.superb-ai.com/ https://neptune.ai/blog/annotation-tool-comparison-deep-learning-data-annotation?utm_source=linkedin&utm_medium=post&utm_campaign=blog-annotation-tool-comparison-deep-learning-data-annotation Diffgram,Label Studio ,CVAT,SuperAnnotate,Datasaur https://anthony-sarkis.medium.com/the-5-best-ai-data-annotation-platforms-for-machine-learning-2021-ec17c15142f3 https://foobar167.medium.com/open-source-free-software-for-image-segmentation-and-labeling-4b0332049878 ***Label Assist: Model Assisted Pre-Annotation for Computer Vision https://blog.roboflow.com/announcing-label-assist/ https://www.youtube.com/watch?v=919CihTlkZw&feature=youtu.be*** https://github.com/jsbroks/awesome-dataset-tools makeml https://makeml.app/ superannotate https://www.superannotate.com/ jupyter-innotater data annotator for Jupyter notebooks https://github.com/ideonate/jupyter-innotater JupyterLab extension for annotating data https://github.com/explosion/jupyterlab-prodigy semi-auto-image-annotation-tool https://github.com/virajmavani/semi-auto-image-annotation-tool labelimage:- https://github.com/wkentaro/labelme , https://github.com/tzutalin/labelImg labelCloud lightweight tool for labeling 3D bounding boxes in point clouds https://github.com/ch-sa/labelCloud labeller https://www.labellerr.com/ prodigy Radically efficient machine teaching An annotation tool powered by active learning https://prodi.gy/ Labelbox-https://labelbox.com/ Playment-https://playment.io/ SuperAnnotate -https://www.superannotate.com/ CVAT-https://github.com/openvinotoolkit/cvat Lionbridge- https://lionbridge.ai/ LinkedAI: A No-code Data Annotations- https://analyticsindiamag.com/linkedai/ Dataturks V7 Darwin The Rapid Image Annotator https://docs.v7labs.com/docs/loading-a-dataset-in-python https://github.com/v7labs/darwin-py#usage-as-a-python-library https://waliamrinal.medium.com/top-and-easy-to-use-open-source-image-labelling-tools-for-machine-learning-projects-ffd9d5af4a20 https://github.com/heartexlabs/awesome-data-labeling Label a Dataset with a Few Lines of Code https://eric-landau.medium.com/label-a-dataset-with-a-few-lines-of-code-45c140ff119d https://analyticsindiamag.com/complete-guide-to-data-labelling-tools/ https://neptune.ai/blog/data-labeling-software Extraction of Objects In Images and Videos Using 5 Lines of Code https://towardsdatascience.com/extraction-of-objects-in-images-and-videos-using-5-lines-of-code-6a9e35677a31 https://neptune.ai/blog/data-labeling-software?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-data-labeling-software 23)tensorflow_datasets as tfds https://www.tensorflow.org/datasets (import tensorflow_datasets as tfds) https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/ 24)https://datasets.bifrost.ai/?ref=producthunt 25)https://ourworldindata.org/ 26)https://data.worldbank.org/ 27)google open images:https://storage.googleapis.com/openimages/web/download.html 30 Largest TensorFlow Datasets for Machine Learning https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/ https://cloud.google.com/bigquery/public-data/ https://towardsdatascience.com/bigquery-public-datasets-936e1c50e6bc https://christopherzita.medium.com/how-to-download-google-images-using-python-2021-82e69c637d59 28)https://data.gov.in/ 29)imagenet dataset-http://www.image-net.org/ 30)https://parulpandey.com/2020/08/09/getting-datasets-for-data-analysis-tasks%e2%80%8a-%e2%80%8aadvanced-google-search/ 31)https://storage.googleapis.com/openimages/web/index.html , https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=segmentation&r=false&c=%2Fm%2F09qck https://console.cloud.google.com/marketplace/browse?filter=solution-type:dataset&_ga=2.35328417.1459465882.1589693499-869920574.1589693499 https://catalog.data.gov/dataset?groups=education2168#topic=education_navigation https://vincentarelbundock.github.io/Rdatasets/datasets.html 32)coco dataset https://cocodataset.org/#explore 33)huggingface datasets-https://github.com/huggingface/datasets https://huggingface.co/datasets https://huggingface.co/languages pip install datasets 34)Big Bad NLP Database-https://datasets.quantumstat.com/ fast.ai Datasets https://course.fast.ai/datasets https://github.com/niderhoff/nlp-datasets 600 NLP Datasets and Glory https://pub.towardsai.net/600-nlp-datasets-and-glory-4b0080bf5ab nlp-datasets https://github.com/karthikncode/nlp-datasets https://analyticsindiamag.com/15-most-important-nlp-datasets/ https://medium.com/ai-in-plain-english/25-free-datasets-for-natural-language-processing-57e407402c60 35)https://www.edureka.co/blog/25-best-free-datasets-machine-learning/ 36)bigquery public dataset ,Google Public Data Explorer https://cloud.google.com/public-datasets https://guides.library.cmu.edu/machine-learning/datasets 37)inbuilt library data eg:iris dataset,mnist dataset,etc... pandas-datareader https://github.com/pydata/pandas-datareader tf.data.Datasets for TensorFlow Datasets 38)https://data.gov.sg/ https://data.gov.au/ https://data.europa.eu/euodp/en/data https://data.europa.eu/euodp/en/data https://data.govt.nz/ data.gov.be ,data.egov.bg/ ,data.gov.cz/english ,portal.opendata.dk,govdata.de,opendata.riik.ee,data.gov.ie,data.gov.gr,datos.gob.es,data.gouv.fr,data.gov.hr dati.gov.it,data.gov.cy,opendata.gov.lt,data.gov.lv,data.public.lu,data.gov.mt,data.overheid.nl,data.gv.at,danepubliczne.gov.pl,dados.gov.pt,data.gov.ro,podatki.gov.si data.gov.sk,avoindata.fi,oppnadata.se,https://data.adb.org/ ,https://data.iadb.org/ ,https://www.weforum.org/agenda/2018/03/latin-america-smart-cities-big-data/ https://data.fivethirtyeight.com/ , https://wiki.dbpedia.org/ ,https://www.europeandataportal.eu/en ,https://data.europa.eu/ ,https://www.census.gov/, https://www.who.int/data/gho ,https://data.unicef.org/open-data/ ,http://data.un.org/ ,https://data.oecd.org/ ,https://data.worldbank.org/ 39.Awesome Public Dataset- https://github.com/awesomedata/awesome-public-datasets Get OpenML’s Dataset in One Line of Code https://mathdatasimplified.com/2021/04/23/fetch_openml-get-openmls-dataset-in-one-line-of-code/ https://github.com/the-pudding/data datasets https://github.com/benedekrozemberczki/datasets kdnuggets https://www.kdnuggets.com/datasets/index.html Hub https://github.com/activeloopai/Hub 40.Datasets for Machine Learning on Graphs-https://ogb.stanford.edu/ 41.https://www.johnsnowlabs.com/data/ 42.30 largest tensorflow datasets-https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/ 43. coco dataset-https://cocodataset.org/#home flickr-downloader https://github.com/renatoviolin/flickr-downloader/ Google Open images-https://opensource.google/projects/open-images-dataset https://storage.googleapis.com/openimages/web/index.html 50+ Object Detection Datasets-https://medium.com/towards-artificial-intelligence/50-object-detection-datasets-from-different-industry-domains-1a53342ae13d 70+ Image Classification Datasets from different Industry domains-https://medium.com/towards-artificial-intelligence/70-image-classification-datasets-from-different-industry-domains-part-2-cd1af6e48eda VisualData Discovery https://www.visualdata.io/discovery https://guides.library.cmu.edu/machine-learning/datasets data https://storage.googleapis.com/openimages/web/visualizer/index.html?c=%2Fm%2F04yqq2&r=false&set=train&type=segmentation&utm_campaign=Weekly%20Machine%20Learning%20news&utm_medium=email&utm_source=Revue%20newsletter VisualData https://www.visualdata.io/discovery bifrost- https://datasets.bifrost.ai/ satellite images https://towardsdatascience.com/finding-satellite-images-for-your-data-science-project-888695361925 https://public.roboflow.com/ https://www.visualdata.io/discovery http://www.image-net.org/ https://www.cs.toronto.edu/~kriz/cifar.html tensorflow_datasets.object_detection - https://storage.googleapis.com/openimages/web/index.html https://github.com/google-research-datasets/Objectron/ https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html?m=1 http://idd.insaan.iiit.ac.in/ http://database.mmsp-kn.de/koniq-10k-database.html https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html https://www.visualdata.io/discovery https://blogs.bing.com/maps/2019-03/microsoft-releases-12-million-canadian-building-footprints-as-open-data https://blogs.bing.com/maps/2019-09/microsoft-releases-18M-building-footprints-in-uganda-and-tanzania-to-enable-ai-assisted-mapping https://datasets.bifrost.ai/ https://storage.googleapis.com/openimages/web/download.html https://computervisiononline.com/datasets http://yacvid.hayko.at/ https://www.cogitotech.com/use-cases/biodiversity/ ImageNet data -http://image-net.org/ ApolloScape Dataset-http://apolloscape.auto/ https://github.com/chrieke/awesome-satellite-imagery-datasets 44.https://github.com/fivethirtyeight/data 45.Recommender Systems Datasets-https://cseweb.ucsd.edu/~jmcauley/datasets.html 46.indiadataportal-https://indiadataportal.com/ 47.US Government Open Dataset: https://www.data.gov/ https://censusreporter.org/ https://data.census.gov/cedsci/ 48.AWS Public Data Sets:https://registry.opendata.aws/ https://aws.amazon.com/opendata/?wwps-cards.sort-by=item.additionalFields.sortDate&wwps-cards.sort-order=desc 49.https://the-eye.eu/public/AI/pile_preliminary_components/ Reddit -https://www.reddit.com/r/datasets/ wikipedia-https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research http://opendata.cern.ch/ , https://www.imf.org/en/Data Global Health Observatory data repository-https://apps.who.int/gho/data/node.main CERN Open Data Portal-http://opendata.cern.ch/ TensorFlow Datasets https://www.tensorflow.org/datasets 50.openblender- https://www.openblender.io/#/welcome 51.Top 10 Datasets For Cybersecurity Projects- https://analyticsindiamag.com/top-10-datasets-for-cybersecurity-projects/ 52.Datasets from Web Crawl Data (nlp)-http://data.statmt.org/cc-100/ 53.https://www.springboard.com/blog/free-public-data-sets-data-science-project/ 54.NASA - https://nasa.github.io/data-nasa-gov-frontpage/ace 55.Academic Torrents,GitHub Datasets,CERN Open Data Portal,Global Health Observatory Data Repository 56.32 Data Sets to Uplift your Skills in Data Science-https://blog.datasciencedojo.com/data-sets-data-science-skills/?utm_content=144243072&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012 https://lionbridge.ai/datasets/the-50-best-free-datasets-for-machine-learning/ 57.OpenDaL-https://opendatalibrary.com/ Data Is Plural-https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/edit#gid=0 VisualData-https://www.visualdata.io/discovery https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f 58.Pandas Data Reader-https://pandas-datareader.readthedocs.io/en/latest/remote_data.html 59.ieee-dataport-https://ieee-dataport.org/datasets https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/data/datasets.md#datasets-and-sources-of-raw-data 60.Generating Realistic Fake Data https://towardsdatascience.com/free-resources-for-generating-realistic-fake-data-da63836be1a8 Full Synthetic Data ,Partial Synthetic Data,Hybrid Synthetic Data Faker is a Python package that generates fake data-https://github.com/joke2k/faker ydata-synthetic,Gretel,gretel-synthetics,GenerateData,DataSynthesizer,SDV,SDGym,SDMetrics,Copulas,gretel-synthetics,kubric,CTGAN,Synthea,synthia,nbsynthetic ,pydbgen,synthpop,faker,Tonic,ydata,Mostly AI,Mirry.ai,Hazy,Gretel,Diveplane,Datagen,Mimesis,faker,FauxFactory,Radar,PikaAccelario,Chooch,Datagen,Datomize,Deep Vision Data,Monitaur,MOSTLY AI,OpenSynthetics,Replica Analytics,Scale AI,SKY ENGINE AI,Synthesis AI,Plaitpy,TimeseriesGenerat,Accelario,Chooch,dgutils,AI.Reverie,Kinetic Vision,SynthDet,OpenSynthetics,Mockaroo,GenerateData,JSON Schema Faker,FakeStoreAPI,Mock Turtle,nbsynthetic,AiFi,AI.Reverie,Anyverse,Cvedia,DataGen,Diveplane,Gretel,Hazy,Mostly AI,OneView,TRGD,YDATA Synthetic,SDV,Tonic.AI,Mostly.AI,Parallel Domain,Mindtech,Synthesis AI,Oneview,Hazy,CVEDIA,SKY ENGINE AI,Edgecase.ai,Statice,ANYVERSE,Rendered.ai,Datomize,Facteus,Gretel,Synthesized,Syntheticus,Syntho,Tonic, kubric,Stable Diffusion,Parallel Domain,Mindtech,Synthesis AI,Oneview,MOSTLY AI,Hazy,CVEDIA,SKY ENGINE AI,Edgecase.ai,Statice,ANYVERSE,Rendered.ai,Datomize,Facteus,Gretel,Synthesized,Syntheticus,Syntho,Tonic,MOSTLY AI, GenRocket, YData, Hazy, and MDClone ,Gretel, MOSTLY AI, Hazy, Statice ,NVIDIA Omniverse, OneView, CVEDIA, Datagen, Parallel Domain,Infinity AI,Parallel Domain,Rendered.AI,Scale.AI,SKY ENGINE AI,Synthesis AI,Paella,statice,DataSynthesizer,Pydbgen,TimeseriesGenerator,Mimesis,Synthesized,Syntheticus,Syntho,Tonic,Clearbox AI ,RDT (Reversible Data Transforms),DeepEcho Models: GANs, CTGAN, WGAN, WGAN-GP, VAEs,GANs, TimeGAN, AR GAN-based Deep Learning data synthesizer CTGAN,CopulaGAN,Synthetic Data Vault,Probabilistic AutoRegressive model Extract the metadata using DataDescriber, Compare the input and synthetic data using ModelInspector Mockaroo https://www.mockaroo.com/ GenerateData https://site.generatedata4.com/ JSON Schema Faker https://json-schema-faker.js.org/ FakeStoreAPI https://fakestoreapi.com/ graviti dataset https://gas.graviti.com/open-datasets Synthetic data for computer vision https://github.com/ZumoLabs/zpy GANs for Tabular Synthetic Data Generation https://github.com/Diyago/GAN-for-tabular-data Synthetic Image Datasets https://analyticsindiamag.com/unity-launches-synthetic-image-datasets-to-train-ai-models-faster/ Synthetic structured data generators https://github.com/ydataai/ydata-synthetic gretel Synthetic Data API https://gretel.ai/ Timeseries DGAN https://synthetics.docs.gretel.ai/en/latest/models/timeseries_dgan.html DatasetGAN: an automatic procedure to generate massive datasets of high-quality images Generating synthetic tabular data with GANs,Synthetic Time-Series Data by A GAN approach Unity Launches Synthetic Image Datasets https://www.marktechpost.com/2021/04/23/unity-launches-synthetic-image-datasets-to-train-ai-and-computer-vision-models-faster/ Generate Your Own Dataset using GAN https://www.analyticsvidhya.com/blog/2021/04/generate-your-own-dataset-using-gan/ accurate of synthetic data https://gretel.ai/blog/how-accurate-is-my-synthetic-data Synthetic data library https://github.com/finos/datahub https://github.com/agmmnn/awesome-blender https://opendata.blender.org/ https://www.youtube.com/watch?v=eZwOeBkLL8E https://www.kdnuggets.com/2019/09/scikit-learn-synthetic-dataset.html Fully Synthetic Data,Partially Synthetic Data ,Hybrid Synthetic Data https://towardsdatascience.com/synthetic-data-key-benefits-types-generation-methods-and-challenges-11b0ad304b55 Synthetic Image Datasets https://analyticsindiamag.com/unity-launches-synthetic-image-datasets-to-train-ai-models-faster/ https://dockship.io/articles/607847e461373d1b994cc2dc/create-synthetic-images-using-opencv-(python) gretel-synthetics Synthetic data generators for structured and unstructured text, featuring differentially private learning. https://github.com/gretelai/gretel-synthetics Synthetic Data Generation Using Gaussian Mixture Model https://deepnote.com/@chanakya-vivek-kapoor/Synthetic-Data-Generation-QaaTRs73T2iCb0amHFbwpQ Synthetic Data Vault https://analyticsindiamag.com/guide-to-synthetic-data-vault-an-ecosystem-of-synthetic-data-generation-libraries/ https://github.com/sdv-dev/SDV Create Your own Image Dataset using Opencv https://www.analyticsvidhya.com/blog/2021/05/create-your-own-image-dataset-using-opencv-in-machine-learning/ ydata-synthetic https://github.com/ydataai/ydata-synthetic Table Evaluator About Evaluate real and synthetic datasets with each other https://github.com/Baukebrenninkmeijer/table-evaluator evaluate quality and efficacy of synthetic datasets SDMetrics https://github.com/sdv-dev/SDMetrics 61.Text Data Annotator Tool - Datasaur https://datasaur.ai/ Tagalog is our state-of-the-art solution for data management and labeling in Natural Language Processing https://www.tagalog.ai/tagalog/ 62.Google Analytics cost data import https://segmentstream.com/google-analytics?utm_source=twitter&utm_medium=cpc&utm_campaign=ga_costs_import_en&utm_content=guide 63.https://lionbridge.ai/services/crowdsourcing/ https://lionbridge.ai/ https://www.clickworker.com/ https://appen.com/ https://www.globalme.net/ 64.Azure Open Datasets https://azure.microsoft.com/en-us/services/open-datasets/ https://azure.microsoft.com/en-in/services/open-datasets/catalog/ Yelp Open Dataset https://www.yelp.com/dataset https://data.world/ ODK Open Data Kit- https://getodk.org/ World Bank Open Data https://data.worldbank.org/ https://analyticsindiamag.com/10-biggest-data-breaches-that-made-headlines-in-2020/ https://data.mendeley.com/ https://github.com/iamtekson/geospatial-data-download-sites https://eugeneyan.com/writing/data-discovery-platforms/ 65.https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f https://towardsdatascience.com/data-repositories-for-almost-every-type-of-data-science-project-7aa2f98128b https://github.com/MTG/freesound-datasets https://dataform.co/ https://github.com/rfordatascience/tidytuesday https://www.youtube.com/watch?v=vCBeGLpvoYM https://www.analyticsvidhya.com/blog/2020/12/top-15-datasets-of-2020-that-every-data-scientist-should-add-to-their-portfolio/?utm_source=linkedin&utm_medium=AV|link|high-performance-blog|blogs|44181|0.375 https://cseweb.ucsd.edu/~jmcauley/datasets.html 66.https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research https://archive.org/details/datasets https://commoncrawl.org/ https://www.youtube.com/watch?v=1aUt8zAG09E 67. 6 Sources of Financial Data https://medium.datadriveninvestor.com/financial-data-431b75975bb yfinance for finance data using https://github.com/ranaroussi/yfinance https://medium.com/towards-artificial-intelligence/algorithmic-trading-with-python-and-machine-learning-part-1-47c56706c182 import fix_yahoo_finance as yf , yahoofinancials ,Pandas DataReaders,Twelve Data financeapi https://towardsdatascience.com/pull-and-analyze-financial-data-using-a-simple-python-package-83e47759c4a7 Investing.com pip install investpy ,Kite by Zerodha pip install kiteconnect,quandl pip install quandl https://www.analyticsvidhya.com/blog/2021/01/bear-run-or-bull-run-can-reinforcement-learning-help-in-automated-trading/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29 Downloading Historical Stock prices with Alpha Vantage https://medium.com/towards-artificial-intelligence/downloading-historical-stock-prices-with-alpha-vantage-688edad46a6d Pandas Datareader https://pandas-datareader.readthedocs.io/en/latest/ https://www.youtube.com/watch?v=f2BCmQBCwDs Get Financial Data Directly into Python https://www.quandl.com/tools/python https://medium.com/nerd-for-tech/how-to-get-financial-data-using-python-7a508f25fc39 openml https://www.openml.org/search?type=data https://registry.opendata.aws/ voice_datasets https://github.com/jim-schwoebel/voice_datasets Dynamically-Generated-Hate-Speech-Dataset https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset 68.DOCANO, an open source text annotation tool https://github.com/doccano/doccano 69.https://www.dataquest.io/blog/free-datasets-for-projects/ 70.audio set https://research.google.com/audioset/ 71.FlatData Flat explores how to make it easy to work with data in git and GitHub https://octo.github.com/projects/flat-data?utm_campaign=Data_Elixir&utm_source=Data_Elixir_337 72.Snorkel is an open-source Python library for programmatically building training datasets without manual labeling. https://www.snorkel.org/ https://towardsdatascience.com/snorkel-programmatically-build-training-data-in-python-712fc39649fe ***2.Feature engineering*** https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd Feature-engine https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c https://feature-engine.readthedocs.io/en/latest/ https://github.com/solegalli/feature_engine https://www.datasciencecentral.com/profiles/blogs/feature-engine-python-package-for-feature-engineering Automated feature engineering https://medium.com/ibm-data-ai/automated-feature-engineering-for-relational-data-with-autoai-3612fafe9f89 Automated Data Wrangling https://catalyst.coop/2021/05/23/automated-data-wrangling/ Automatic Feature Engineering Using Featurewiz https://towardsdatascience.com/automate-your-feature-selection-workflow-in-one-line-of-python-code-3d4f23b7e2c4 https://github.com/AutoViML/featurewiz Automatic Feature Engineering Using AutoFeat https://analyticsindiamag.com/guide-to-automatic-feature-engineering-using-autofeat/ Upgini accuracy improving features https://github.com/upgini/upgini https://upgini.com/ Categorical Encoding https://github.com/scikit-learn-contrib/category_encoders lazytransform https://github.com/AutoViML/lazytransform Streamlining Feature Engineering Pipelines with Feature-engine https://towardsdatascience.com/streamlining-feature-engineering-pipelines-with-feature-engine-e781d551f470 https://feature-engine.readthedocs.io/en/latest/# Validate your Data (Schema) https://towardsdatascience.com/introduction-to-schema-a-python-libary-to-validate-your-data-c6d99e06d56a Validate Your pandas DataFrame with Pandera https://github.com/pandera-dev/pandera Statistical DataFrame Testing Toolkit https://pandera.readthedocs.io/en/stable/index.html Data storing format:Pickle,Parquet,Feather,Avro,ORC Data cleaning-Pyjanitor-https://analyticsindiamag.com/beginners-guide-to-pyjanitor-a-python-tool-for-data-cleaning/ data cleaning library https://www.analyticsvidhya.com/blog/2021/05/data-cleaning-libraries-in-python-a-gentle-introduction/ Mage https://github.com/mage-ai/mage-ai Cleaner Data Analysis with Pandas Using Pipes https://towardsdatascience.com/cleaner-data-analysis-with-pandas-using-pipes-4d73770fbf3c DataPrep https://dataprep.ai/ https://github.com/sfu-db/dataprep https://towardsdatascience.com/dataprep-v0-3-0-has-been-released-be49b1be0e72 Dora (pip library) - data cleaning Dora,PrettyPandas,DataCleaner,Tabulate,Pyjanitor,OpenRefine,cleanlab,pandera https://github.com/sfu-db/dataprep https://github.com/akanz1/klib https://www.bitrook.com/ https://github.com/rhiever/datacleaner https://github.com/johnkerl/miller cleanlab data-centric AI and machine learning with label errors, finding mislabeled data, and uncertainty quantification. Works with most datasets and models https://github.com/cleanlab/cleanlab cleantext https://www.youtube.com/watch?v=i2TjAgga1YU&feature=youtu.be CleanText: A Python Package to Clean Raw Text Data https://analyticsindiamag.com/guide-to-cleantext-a-python-package-to-clean-raw-text-data/ ATOM https://github.com/tvdboom/ATOM https://towardsdatascience.com/how-to-test-multiple-machine-learning-pipelines-with-just-a-few-lines-of-python-1a16cb4686d openrefine A free, open source, powerful tool for working with messy data https://openrefine.org/# data leaning library https://www.analyticsvidhya.com/blog/2021/05/data-cleaning-libraries-in-python-a-gentle-introduction/ https://machinelearningmastery.com/basic-data-cleaning-for-machine-learning/ Speed Up Data Cleaning and Exploratory Data Analysis in Python with klib https://github.com/akanz1/klib https://towardsdatascience.com/speed-up-your-data-cleaning-and-preprocessing-with-klib-97191d320f80 missingno https://github.com/ResidentMario/missingno Take the Pain Out of Data Cleaning for Machine Learning https://towardsdatascience.com/take-the-pain-out-of-data-cleaning-for-machine-learning-20a646a277fd dabl https://ms-bharti.medium.com/jump-start-your-supervised-learning-task-with-dabl-e479323e81fe Easy to use Python library of customized functions for cleaning and analyzing data https://github.com/akanz1/klib PyOD https://pyod.readthedocs.io/en/latest/ https://github.com/yzhao062/pyod/blob/development/docs/index.rst https://towardsdatascience.com/how-to-detect-outliers-with-python-pyod-aa7147359e4b Amazon’s New Visual Data Cleaning Tool Can Speed Up Your AI Projects https://medium.com/dataseries/how-amazons-new-visual-data-tool-can-speed-up-your-ai-projects-68e3289382c Featuretools https://www.featuretools.com/ https://towardsdatascience.com/why-automated-feature-engineering-will-change-the-way-you-do-machine-learning-5c15bf188b96 https://github.com/alteryx/featuretools https://analyticsindiamag.com/introduction-to-featuretools-a-python-framework-for-automated-feature-engineering/ Feature Selection using Genetic Algorithm https://github.com/kaushalshetty/FeatureSelectionGA AutoFeat https://analyticsindiamag.com/guide-to-automatic-feature-engineering-using-autofeat/ https://github.com/cod3licious/autofeat feast Feature Store for Machine Learning https://github.com/feast-dev/feast https://www.youtube.com/watch?v=ZeJdr0nZ9PA Category Encoders https://contrib.scikit-learn.org/category_encoders/ Feature-engine https://feature-engine.readthedocs.io/en/latest/index.html FeatureTools,AutoFeat,TsFresh,Cognito,OneBM,ExploreKit,PyFeat,Category Encoders,Feature-engine Automated Feature Selection: Featurewiz https://github.com/AutoViML/featurewiz https://towardsdatascience.com/featurewiz-fast-way-to-select-the-best-features-in-a-data-9c861178602e zoofs a Python library for performing feature selection https://github.com/jaswinder9051998/zoofs Feature Engineering of DateTime Variables for Data Science, Machine Learning https://www.kdnuggets.com/2021/04/feature-engineering-datetime-variables-data-science-machine-learning.html NeatText a simple NLP package for cleaning textual data and text preprocessing https://github.com/Jcharis/neattext Remove duplicate data in dataset,Data validity check,Contaminated Data,Inconsistent Data,Invalid Data, Feature Selection 1.Removal of arbitraty features: DropFeatures Removing unused columns,Removing Constant features,Removing Constant Features using VarianceThreshold,Removing Quasi-Constant Features,Removing Duplicate Columns 2.Removal of constant and almost constant features: DropConstantFeatures Removal of Low Variance removal of irrelevant data 3.Removal of duplicated variables: DropDuplicateFeatures 4.Removal of correlated features: DropCorrelatedFeatures, SmartCorrelatedSelection Drop features that have a poor correlation with the response variable 5.Selection of features by value shuffling: SelectByShuffling Selection of features by High correlation with the target variable 6.Selection of features by univariate performance: SelectBySingleFeaturePerformance 7.Selection of features by target encoding: SelectByTargetMeanPerformance 8.Recursive Feature Elimination: RecursiveFeatureElimination 9.Recursive Feature Addition: RecursiveFeatureAddition stats,Scipy,Pingouin,Statsmodels,SymPy,Sage, StatisticsGen component computes statistics Check data types , Handle duplicate values a.Handle missing value Types of missing value https://datamuni.com/@atsunorifujita/missing-value-imputation-using-datawig Handling Missing Values in Pandas https://pub.towardsai.net/handling-missing-values-in-pandas-f87cec928937 Identify the source of missing data i.missing completely at random(no correlation b/w missing and observed data) we can delete no disturbance of data distribution ii.missing at random (randomness in missing data, missing value have correlation by data) we can't delete because disturbance of data distribution iii.missing not at random (there is reason for missing value and directly related to value) iv.structured missing 100 % sure on why it is missing Identify Missingness Types With Missingno https://towardsdev.com/how-to-identify-missingness-types-with-missingno-61cfe0449ad9 Univariate,Multivariate https://medium.com/fintechexplained/what-are-imputers-in-data-science-b72f8308322b univariate imputation impute on 1 column multi variate imputation impute on 1 or more column 1.if missing data too small then delete it a.row deletion b.column deletion c.pairwise deletion and listwise deletion Drop based on a threshold value,Drop using a subset of columns 2.replace by statistical method mean(influenced by outiler),median(not influenced by outiler),mode , minimum, maximum,Zero,Constant Fill with Mean / Median of Column or Group Forward Fill or Forward Fill within Groups Mean and Median Fill with Groupby Pass another DataFrame to fillna function to fill up the missing values. Similar case Imputation 3.apply classifier algorithm to predict missing value Using Algorithms that support missing values Imputation using Deep Learning Library — Datawig https://github.com/awslabs/datawig 4.Simple Imputer,and Multiple Imputation ,Iterative imputer,knn imputer, multivariate imputation, Verstack — NaNImputer,Impyute —MICE ,Substitution 5.apply unsupervised 6.Random Imputation,Iterative Imputation,Random Sample imputation 7.Adding a variable to capture NAN(missing term),Imputation with the string ‘Missing’,Adding missing idicator 8.Arbitrary Value Imputation TREAT MISSING VALUES AS A SEPARATE CATEGORY ue DOMAIN KNOWLEDGE 9.hot deck Imputation,Cold deck imputation 10.regression Imputation,Stochastic Regression Imputation,Interpolation and Extrapolation 11.End of Distribution Imputation 12.Arbitrary Value Imputation 13.Frequent Category Imputation 14.MICE Imputation,miceforest ( https://github.com/AnotherSamWilson/miceforest ) Miss Forest https://github.com/stekhoven/missForest 15.interpolation https://www.analyticsvidhya.com/blog/2021/06/power-of-interpolation-in-python-to-fill-missing-values/ Interpolate or Interpolate within Groups LINEARINTERPOLATION ,POLYNOMIALINTERPOLATION,INTERPOLATION THROUGH PADDING Extrapolation and Interpolation ,Time-Based Interpolation,Spline Interpolation,Linear Interpolation,Smoothing, interpolation,Bidirectional Recurrent Imputation for Time Series ( 16.Last Observation Carried Forward (LOCF) , Next Observation Carried Backward , Rolling Statistics, Interpolation Single and Multiple Imputation,Univariate Imputation,Multivariate Imputation ,Iterative Imputer,MissForest Imputation,Stochastic Regression Imputation, Multiple Imputations, Datawig, Hot-Deck imputation, Extrapolation, Interpolation datawig Imputation of missing values in tables https://github.com/awslabs/datawig Imputation using K-NN,missForest,Random Forest-based Imputation,missingpy,som,Ann,mlp Model based procedure gaussian mixture model Imputation Using Deep Learning (Datawig),neural network for imputation,BRITS 15.autoimpute-https://github.com/kearnz/autoimpute 16.bfill / ffill Back Fill or Back Fill within Groups 17.Adding a variable to capture NAN 18.replace NAN with a new category 19.Missing indicator After drop or imputation feature distribution should be same https://www.kdnuggets.com/2021/05/deal-with-categorical-data-machine-learning.html https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779 https://stefvanbuuren.name/fimd/want-the-hardcopy.html https://www.datasciencecentral.com/profiles/blogs/how-to-treat-missing-values-in-your-data-1 20.Imputation with the string ‘Missing’ ,Addition of binary missing indicators 21.Algorithms robust to missing values - LightGBM datawig imputation https://github.com/awslabs/datawig 22.Cluster-based approach for missing value imputation Naive clustering,Column-sensitive clustering Top Data Cleaning Tools https://www.marktechpost.com/2022/02/20/top-data-cleaning-tools-for-data-science-and-machine-learning-projects-in-2022/ OpenRefine https://openrefine.org/ https://github.com/OpenRefine/OpenRefine Data Ladder https://dataladder.com/ re-data fix data issues https://github.com/re-data/re-data Automatically find and fix errors in your ML datasets. https://github.com/cleanlab/cleanlab Clean APIs for data cleaning https://github.com/pyjanitor-devs/pyjanitor datacleaner https://github.com/rhiever/datacleaner https://github.com/akanz1/klib https://pyjanitor-devs.github.io/pyjanitor/ https://dataprep.ai/ https://scrubadub.readthedocs.io/en/latest/index.html https://www.bitrook.com/ AutoClean https://github.com/elisemercury/AutoClean Dora,PrettyPandas,DataCleaner,Tabulate,Pyjanitor b.Handle imbalance Collect More Data if possible,Try Resampling Your Dataset 1.Under Sampling - mostly not prefer because lost of data imbalaced-learn,tomek links,Random Under-Sampling, Edited Nearest Neighbours,NearMiss Random majority under-sampling with replacement,Tomek Links Undersampling,Under-sampling with Cluster Centroids,Condensed Nearest Neighbour,One-Sided Selection,Neighboorhood Cleaning Rule,One-Sided Selection, 2.Over Sampling (RandomOverSampler (here new points create by same dot)) , SMOTETomek(new points create by nearest point so take long time),BorderLine Smote,Borderline-SMOTE SVM,FAIR SMOTE,DBSMOTE,SMOTE-ENN ,KMeans Smote,SVM Smote,SMOTe NC,ENNSMOTE,SVMSMOTE,MOTE-N ADASYN,ADASYN,Smote-NC,Random Over Sampling,RandomUnderSampler,SMOTEN,SMOTE-Tomek,SMOTE-ENN,SMOTE-CUT,Cluster-Based Over Sampling, Informed Over Sampling,MSMOTE,Oversampling Using Gaussian Mixture Models,SMOTE + Tomek Links, SMOTE + ENN,Crucio SMOTEENN,NearMiss,OSS & NCR — under sampling,Borderline SMOTE KNN,Borderline SMOTE SVM,Adaptive Synthetic Sampling (ADASYN),BalancedBaggingClassifier() , BalancedRandomForestClassifier SMOTE-NC Over-sampling followed by under-sampling : SMOTE + Tomek links,SMOTE + ENN smote_variants https://github.com/analyticalmindsltd/smote_variants https://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data-b8155bdbe2b5 https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/ ensmble based -Bagging Based techniques, Boosting-Based techniques,Adaptive Boosting- Ada Boost techniques,Gradient Tree Boosting,XG Boost tools Imb-learn,SMOTE-Variants,Regression-ReSampling https://towardsdatascience.com/tools-to-handle-class-imbalance-bff20c3bf099 Balancing data sets with Crucio ADASYN https://medium.com/softplus-publication/balancing-data-sets-with-crucio-adasyn-79f04ff0779d LoRAS: A Better Oversampling Algorithm https://analyticsindiamag.com/hands-on-guide-to-loras-a-better-oversampling-algorithm/ https://github.com/narek-davtyan/LoRAS https://towardsdatascience.com/7-over-sampling-techniques-to-handle-imbalanced-data-ec51c8db349f Combining Over and Under-sampling 3.class_weight give more importance(weight) to that small class ( Cost-Sensitive Algorithms) from sklearn import compute_class_weight Cost-sensitive learning,Class-balanced loss,Focal loss weighted loss function 4.use Stratified kfold to keep the ratio of classess constantly, train teat spilt startify attribute Use K-fold Cross-Validation in the Right Way,Stratified Cross Validation,repeated K-fold Cross-Validation,Stratified K-fold Cross-Validation Stratified Sampling,Stratified splits 5.Weighted Neural Network cluster based sampling 6.MESA https://analyticsindiamag.com/guide-to-mesa-boost-ensemble-imbalanced-learning-with-meta-sampler/ 7.choose Proper Evaluation Metric metric roc,f1,etc... https://machinelearningmastery.com/framework-for-imbalanced-classification-projects/ https://www.kdnuggets.com/2020/01/5-most-useful-techniques-handle-imbalanced-datasets.html 8.Deep Imbalanced Regression https://github.com/YyzHarry/imbalanced-regression https://analyticsindiamag.com/deep-imbalanced-regression-complete-guide/ Imbalanced Dataset Sampler https://github.com/ufoym/imbalanced-dataset-sampler 9.Ensemble Techniques ensemble techinque - Bagging Based techniques,Boosting-Based techniques BalancedBaggingClassifier,Threshold moving,Easy Ensemble classifier,Balanced Random Forest,Balanced Bagging,RUSBoost,MESA 10.Try Different Algorithms (ensemble techinque - Bagging Based techniques,Boosting-Based techniques) model based (some models are particularly suited for imbalanced dataset) Algorithmic Ensemble Techniques,Tree-Based Algorithms 11.Try a Different Perspective ( consider as anomaly detection or change detection) Threshold Moving Methods,One-Class Classification,Customised Ensemble Algorithms Probability Tuning Algorithms,Calibrating Probabilities,Tuning the Classification Threshold 12.databalancer https://github.com/pradeepdev-1995/databalancer 13.collect more data 14.treat problem as anomaly detection 15.Combined Class Methods In this type of method, various methods are fused together to get a better result to handle imbalance data. For instance, like SMOTE can be fused with other methods like MSMOTE (Modified SMOTE), SMOTEENN (SMOTE with Edited Nearest Neighbours), SMOTE-TL, SMOTE-EL, etc. to eliminate noise in the imbalanced data sets 16.One-Class Algorithms,One-Class Support Vector Machines,Isolation Forests,Minimum Covariance Determinant,Local Outlier Factor,Mahalanobis Distance for One Class Classification 17.BalancedBatchGenerator https://imbalanced-learn.org/stable/references/generated/imblearn.keras.BalancedBatchGenerator.html 18.train_test_split stratify attribute , stratify split 19. https://github.com/pradeepdev-1995/databalancer Meta’s balance package https://github.com/facebookresearch/balance c.Remove noise data d.Format data d.Discretize a.Equal width binning b.Equal frequency binning c.K-means Binning d.Discretization by Decision Trees e.ChiMerge f.Arbitrary Discretization g.Quantile h.Custom Discretization Discretisation plus categorical encoding,Discretisation plus encoding Discretisation with classification trees,Domain knowledge discretisation Data Binning Binning based on distribution (quantile-cut),Binning based on values (cut) Bucketing , quantile bucketing ,Clipping e.Handle categorical data Ordinal,Nominal,cyclic,binary categorical variables 1.One Hot Encoding , dummy, and effect coding,Similarity Encoding,Binary Encoding Rainbow Method for Label Encoding 2.Count Or Frequency Encoding 3.Ordinal encoding,Nominal Encoding,Monotonic ordinal encoding,Target Guided Ordinal Encoding,Target Guided Mean Encoding,Target-Mean-Encoding 4.Target encoding / Mean encoding,GapEncoder,MinHashEncoder,Target guided ordinal encoding,Bayesian Target Encoding Target Encoding,K-Fold Target Encoding,Leave-One-Out Target Encoding,Leave One fold out Target Encoding,Target Encoding with a Weighted Mean 5.Probability Ratio Encoding,Rank Encoding,Polynomial Encoding,Backward Difference Encoding 6.label encoding or .cat.codes ,Label Encoding with Rainbow Method 7.probability ratio encoding 8.woe(Weight_of_evidence) Word2Vec(word Word embedding) 9.one hot encoding with multi category (keep most frequently repeated only) (One hot encoding of top categories) 10.feature hashing,CatBoost Encoding 11.sparse csr matrix 12.entity embeddings,Categorical Embeddings 13.binary encoding,Base-N Encoding 14.Rare label encoding 15.Leave-one-out(Loo) encoding,Generalized Linearn Mixed Model 16.hash encoding,MinHashEncoder,SimilarityEncoder,DatetimeEncoder,SuperVectorizer,FeatureHasher,DictVectorizer,HashingVectorizer,DecisionTreeEncoder 17.dummy encoding,NaN Encoding,bin counting scheme,effect coding scheme 18.Helmert Encoding,Backward Difference Encoding,James-Stein Encoding,M-estimator Encoding,Thermometer Encoder,Bayesian Encoders,Effect Encoding Helmert Encoding,Base N Encoding,Hash Encoding,Effect or Sum or Deviation Encoding,Backward Difference Encoding,M-Estimator Encoding,James- Stein Encoding,Thermometer Encoding,CatBoost Encoding,Backward Difference Encoding,Binary Encoding,NaN encoding Polynomial encoding,Expansion encoding,Probability Ratio,Binary encoding,cat boost encoder,glm encoder,m-estimte,sum coding, polynomial Encoding,PRatioEncoder,DecisionTreeEncoder,Similarity Encoding,BackwardDifferenceEncoder GapEncoder,MinHashEncoder,TargetEncoder,Polynomial Encoding,James-Stein Encoding,MultiLabelBinarizer,SumEncoder,Quantile Encoder,Summary Encoder ,Base N Coding,Leaf Encoding,GLMM Encoding,James-Stein Encoding,Thermometer Encoding,Quantile Encoding,Summary Encoding,Collapsing Categories Transform your categorical columns with imperio SmoothingTransformer entity encoder for categorical variable https://contrib.scikit-learn.org/category_encoders/ Automatically selects the best encoder https://github.com/dirty-cat/dirty_cat Improve ML Model Performance by Combining Categorical Features https://towardsdatascience.com/improve-ml-model-performance-by-combining-categorical-features-a23efbb6a215 https://towardsdatascience.com/beyond-one-hot-17-ways-of-transforming-categorical-features-into-numeric-features-57f54f199ea4 https://towardsdatascience.com/how-to-encode-categorical-data-d44dde313131 https://towardsdatascience.com/python-for-finance-7-useful-libraries-that-you-should-know-e422b9e9aaba f.Scaling of data 1.Normalisation 2.Standardization(StandardScaler) 3.Robust Scaler not influenced by outliers because using of median,IQR 4.Min Max Scaling 5.Mean normalization 6.maximum absolute scaling 7.Power Transformer Scaler 8.Scaling To Median And Quantiles,Scaling to minimum and maximum values,Scaling to the vector norm 9.unit vector scaler 10.Z-score standardization https://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/?utm_source=linkedin&utm_medium=KJ|link|high-performance-blog|blogs|44204|0.375 Probability and Statistics Packages : PyMC3, tensorflow-probability,Pyro,GPyTorch,hmmlearn,pomegranate,GPflow,patsy,pingouin,Orbit Q-Q plot or Shapiro-Wilk Normality Test or lilliefors test or Jarque-Bera test or Kolmogorov-Smirnov or Anderson-Darling test is used to check whether feature is guassian or normal distributed required for linear regression,logistic regression to Improve performance if not distributed then use below methods to bring it guassian distribution normal test,Histogram,Q-Q plot,KDE plot,Skewness and Kurtosis for check normal distribution Fitter Library Finding the Best Distribution that Fits Your Data https://towardsdatascience.com/finding-the-best-distribution-that-fits-your-data-using-pythons-fitter-library-319a5a0972e9 anderson teset use for check any distribution Basic Distributions - PDF, PMF, CDF, PPF,Unform, Gaussian, Bernoulli, Multinomial,Normal Distribution,Poisson, Exponential, Geometric, Log-normal distribution, Pareto/Power Law Distribution b.Logarithmic Transformation,LogCpTransformer c.Reciprocal Trnasformation d.Square Root Transformation e.Exponential Transdormation f.BoxCOx and Yeo-Johnson Transformation g.log(1+x) Transformation h.johnson i.power transformations https://towardsdatascience.com/when-and-how-to-use-power-transform-in-machine-learning-2c6ad75fb72e g.Quantile Transformation ,Arcsin Transformation , Inverse of Log,Inverse of Exponential,Inverse of Square Root,Square of Log,Square root of Exponential Root transformation,Cube root transformation,Cosine Transformation,SplineTransformer,FunctionTransformer,ArcsinTransformer Left skewness (use powers) Squares transformation,Cubes transformation,High powers g.Remove low variance feature by using VarianceThreshold remove Duplicate data,Low variation data,Irrelevant data,Incorrect data remove Low entropy of categorical attributes h.Same variable(only 1 variable) in feature then remove feature i.Outilers removing outilers depond on problem we are solving https://github.com/jainyk/package-outlier 2 type of outilers available: Global outiler(single value/data point that deviates from the distribution), Local outiler,Contextual (conditional) outliers,Collective outliers(Group of datapoint deviates from the distribution) eg: incase of fraud detection outilers are very important methods to find outiler: Tukey’s fences ,KNN distance,Autoencoders,Standard Deviation,zscore,boxplot,scatter plot,histogram,Violin Plot,IQR,TensorFlow_Data_Validation,svm,One-Class SVM,Isolation Forest,kmeans,DBSCAN,K Means Clustering,Percentile,knn,autoencoder,local outiler factor,One-Class Classification,Medıan Absolute Devıatıon Automatic Outlier Detection:Isolation Forest,DBSCAN,Local Outlier Factor,Standard Deviation Approach,K Means Clustering,Minimum Covariance Determinant,Robust Random Cut Forest,DBScan Clustering,One-Class Classification,One-Class SVM,Autoencoder,Outlier Detection using In-degree Number,Histogram-based Outlier Detection,Robust Covariance,PyNomaly,angle-based outlier detection (ABOD),k-Nearest Neighbors Detector,Elliptic Envelope,Cluster-based,Local Outlier Factor,Histogram-based Outlier Detection outiler treatment: Keep them,mean/median/random imputation,drop,discretization (binning),Winsorization,treat as seperate group,replace with resperctive percentiles,standardize and scale the data,transformation(log,scaling,sqrt,power),Replace the outlier values with a suitable value (Like 3rd deviation),Percentile Based Flooring and Capping,Binning,Trimming,Treating outliers as missing values,Top/bottom/zero coding,winsorizing,robust scaler,log transformation,binning,regularisation,Discretization,arbitrary value Outlier capping with IQR Outlier capping with mean and std Outlier capping with quantiles Arbitrary capping Separation: If the amount of the outlier is higher than the normal then we can separate them from the main data and fit the model on them separately Use a Different algorithm that is not sensitive to outliers Segment data so outliers are in a separate group Weighted means (which put more weight on the “normal” part of the distribution) Trimming: Remove outliers from dataset. However, it can remove large proportion of data. Capping: No data is removed. However, it distorts variable distribution. Missing data: The outliers are treated as missing data. Discretization: The outliers are put into lower and upper bins. Arbitrary capping: Domain knowledge of the variable is required to cap the min and max Winsorization: Truncate or cap extreme values to reduce the impact of outliers Transformation: Apply logarithmic or square root transformations Modeling techniques: Use robust regression or tree-based models Outlier removal: Remove the values with careful consideration if they pose an extreme challenge Separate Analysis : This involves performing separate analyses for the data with and without outliers Flagging : Create an additional variable to indicate outliers, providing transparency about their presence in the dataset. ML model which are not sensitive to outliers Like:-KNN,Decision Tree,SVM,NaïveBayes,Ensemble PyOD: A Python Toolkit For Outlier Detection https://analyticsindiamag.com/guide-to-pyod-a-python-toolkit-for-outlier-detection/ TODS: An Automated Time-series Outlier Detection System https://github.com/datamllab/tods https://towardsdatascience.com/tods-detecting-outliers-from-time-series-data-2d4bd2e91381 anomalib anomaly detection library https://github.com/openvinotoolkit/anomalib if outiler present then use robust scaling alibi-detect https://github.com/SeldonIO/alibi-detect#adversarial-detection https://docs.seldon.io/projects/alibi-detect/en/latest/ https://medium.com/towards-artificial-intelligence/outlier-detection-and-treatment-a-beginners-guide-c44af0699754 https://towardsdatascience.com/two-outlier-detection-techniques-you-should-know-in-2021-1454bef89331 j.Anomaly anomaly-detection-resources https://github.com/yzhao062/anomaly-detection-resources Types of Anomalies : Point anomalies,Contextual anomalies,Collective anomalies,Group Anomalies,Spatial Anomalies,Temporal Anomalies clustering techniques to find it Timetk https://towardsdatascience.com/timetk-the-r-library-for-time-series-analysis-9822f7720318 Isolation Forest(for Big Data),Z score,dbscan,Local Outlier Factor,One-Class Support Vector Machine,Autoencoders,knn,Time Series Analysis,Elliptic EnvelopeInterquartile Range,Median Absolute Deviation,K-Nearest Neighbours,Fast-MCD,Auto Encoders,K-means,Histogram-based,pca,K-means,Gaussian Mixture Model,Autoencoder,Hidden Markov Models (HMM) 𝐏𝐲𝐎𝐃 Local Correlation Integral (LCI),Histogram-based Outlier Detection (HBOS),Angle-based Outlier Detection (ABOD),Clustering-Based Local Outlier Factor (CBLOF),Minimum Covariance Determinant (MCD),Stochastic Outlier Selection (SOS),Spectral Clustering for Anomaly Detection (SpectralResidual),Feature Bagging,Average KNN,Connectivity-based Outlier Factor (COF),Variational Autoencoder (VAE) bootstrapping to remove the influence of the outlier data Anomaly detection using PyOD https://pyod.readthedocs.io/en/latest/ https://www.youtube.com/watch?v=QPjG_313GOw https://github.com/yzhao062/pyod https://pyod.readthedocs.io/en/latest/pyod.models.html ADBench https://github.com/Minqi824/ADBench Anomaly Detection Pyfbad https://github.com/Teknasyon-Teknoloji/pyfbad divided into three types:Point/Global Anomalies,Collective Anomalies,Contextual Anomalies https://towardsdatascience.com/a-comprehensive-beginners-guide-to-the-diverse-field-of-anomaly-detection-8c818d153995 https://github.com/zhuyiche/awesome-anomaly-detection https://medium.com/@ODSC/data-sciences-role-in-anomaly-detection-bd976f93d7e3 k.Sampling techniques Random Sampling,Systematic Sampling,Cluster Sampling,Weighted Sampling,Stratified Sampling a.biased sampling b.unbiased sampling l.Feature Creation a.Combination of multiple features with mathematical operations b.Combination of multiple features with a reference value ***3.Exploratory Data Analysis(eda)*** Explore the dataset by using python or microsoft Excel,Atoti,Power BI,Datapane’s,Tableau,TabPy,SAS Business Intelligence and Analytics Tool,QlikView,PyToQlik ,KNIME,Splunk,RapidMiner,Zoho Analytics,Sisense etc... TabPy: Combining Python and Tableau https://www.kdnuggets.com/2020/11/tabpy-combining-python-tableau.html atoti https://www.atoti.io/ https://www.youtube.com/watch?v=Hb6mSXa14oo Datapane’s Create a Beautiful Dashboard in Python in a Few Lines of Code https://towardsdatascience.com/datapanes-new-features-create-a-beautiful-dashboard-in-python-in-a-few-lines-of-code-a3c44523292b Switching from Spreadsheets to Neptune.ai https://neptune.ai/blog/switching-from-spreadsheets-to-neptune-ai Data Analysis using excel https://www.excel-easy.com/data-analysis.html https://www.educba.com/data-analysis-tool-in-excel/ https://www.youtube.com/watch?v=OOWAk2aLEfk Power BI In Jupyter Notebooks https://github.com/microsoft/powerbi-jupyter https://analyticsindiamag.com/microsoft-releases-power-bi-in-jupyter-notebooks/ Mito Generating Python By Editing Spreadsheet https://www.youtube.com/watch?v=yy3-C39ra6s https://trymito.io/?source=twitter1 Automate Pivot Table with Python https://towardsdatascience.com/automate-excel-with-python-pivot-table-899eab993966 OpenPyXL: A Python Module For Excel https://analyticsindiamag.com/guide-to-openpyxl-a-python-module-for-excel/ causal interactive dashboards and beautiful visuals https://www.causal.app/, Visual Programming (Orange) https://orange.biolab.si/ Integrating Tableau With Python https://analyticsindiamag.com/tabpy/ Qlib https://analyticsindiamag.com/qlib/ Data visualization (Matplotlib,Seaborn,DASH,Plotly,Plotly-Express,pyqtgraph,Bokeh,Pandas-Bokeh,Pygal,hvplot,holoviews,chartify,lets-plot,pyqtgraph,glue,plotnine,pygal,bqplot,toyplot,chart,itkwidgets,vedo,ipyvolume,pyvista,glumpy,geopandas,pycountry,geopy,geo-py,pypopulation, geotext,folium,cartopy,gmplo,ipyleaflet,geoviews,geoplot,splot,arviz, hypertools,geoplotlib,Geopandas package,choroplethmaps,Leafmap,Dash,Pydot,Geoplotlib,ggplot,visualizer,Greppo,Altair,folium,geoplot,networkx,graphviz,pydot,pygraphviz,python-igraph,pyvis,pygsp,ipycytoscape,nxviz ipydagred3,Diffbot,etc...) Dashboarding : bokeh,dash,streamlit,panel,visdom ,voila,wave,jupyter-flex,ipyflex,pandas_bokeh Openpxl: Automate Excel Reporting Datapane: A Python Library to Build Interactive Reports Scatterplot,Binned Scatterplot,multi line plot,bubble chart,line charts,bar chart,histogram,boxplot, Pie charts,Line Plot,distplot,Histogram Gantt Chart,bubble charts,area plot,heat map,index plot,violin plot,time series plot,density plot,dot plot,strip plot,plotly,Choropleth Map,Kepler,PDF,Kernel density function,networkx,Scatter_matrix,Bootstrap_plot,functionvis,Higher-Dimensional Plots,3-D Plots,3D Plots With Matplotlib,3D Plots With Plotly,Animated Plot With Plotly,Word Clouds,HoloViz,Horizontal Bar Graphs,Stacked Bar Graphs,Group Bar Graphs,Raincloud Plotsradviz,bootstrap_plot,lag_plot,JoyPy plots,Gantt Chart,Box and Whisker Plot,Waterfall Chart,Pictogram Chart,Timeline,highlight Table,Bullet Graph,Choropleth Map,Word Cloud,Network Diagram,Correlation Matrices,Bubble clouds,Cartograms,Circle views,Dendrograms,Dot distribution maps,Open-high-low-close charts,Polar areas,Radial trees,Ring Charts,Sankey diagram,Span charts,Streamgraphs,Treemaps,Wedge stack graphs, table charts,lollipop charts,distplot,floWeaver hvplot A high-level plotting API for the PyData ecosystem built on HoloViews https://hvplot.holoviz.org/ 50-charts https://towardsdatascience.com/how-did-i-classify-50-chart-types-by-purpose-a6b0aa5b812d all in one https://app.learney.me/ Python Tool For Visualizing and Plotting 2D/3D Scientific Data https://analyticsindiamag.com/guide-to-mayavi-a-python-tool-for-visualizing-and-plotting-2d-3d-scientific-data/ patchworklib - combine multiple py charts easily 7 Techniques to Visualize Geospatial Data https://www.kdnuggets.com/2017/10/7-techniques-visualize-geospatial-data.html data to viz https://www.data-to-viz.com/ Interactive plots directly with pandas https://towardsdatascience.com/get-interactive-plots-directly-with-pandas-13a311ebf426 Top 10 Data Visualization Tools https://www.analyticsvidhya.com/blog/2021/04/top-10-data-visualization-tools/ https://www.xenonstack.com/blog/data-visualization-tools/ https://www.analyticsvidhya.com/blog/2021/03/when-to-use-what-plot-a-beginners-guide-to-select-plots-for-visualization/ https://towardsdatascience.com/8-free-tools-to-make-interactive-data-visualizations-in-2021-no-coding-required-2b2c6c564b5b https://datavizproject.com/ https://datavizcatalogue.com/ https://attachments.convertkitcdnm.com/232198/ee18f415-1406-4e5c-94f1-49a2c6e3ec4e/Statistics-The-Big-Picture-Poster.pdf https://towardsdatascience.com/8-free-tools-to-make-interactive-data-visualizations-in-2021-no-coding-required-2b2c6c564b5b HiPlot (high dimensional data)-https://github.com/facebookresearch/hiplot https://levelup.gitconnected.com/learn-hiplot-in-6-mins-facebooks-python-library-for-machine-learning-visualizations-330129d558ac https://towardsdatascience.com/top-6-python-libraries-for-visualization-which-one-to-use-fe43381cd658 https://www.kaggle.com/abhishekvaid19968/data-visualization-using-matplotlib-seaborn-plotly 𝗞𝗲𝗿𝗮𝘀 𝗠𝗼𝗱𝗲𝗹 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿(ann-visualizer)- 𝗽𝗶𝗽𝟯 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗴𝗿𝗮𝗽𝗵𝘃𝗶𝘇 univariate and bivariate and multivariate analysis model visualization Tensorboard,netron,playground tensorflow,plotly,TensorDash,Dash,Microscope,Lucid distributions(discerte,continous) data distributions-normal distribution,Standard Normal Distribution,Student's t-Distribution,Bernoulli Distribution,Binomial Distribution,Poisson Distribution,Uniform Distribution,F Distribution,Covariance and Correlation Pingouin statistical package https://pingouin-stats.org/index.html https://www.youtube.com/watch?v=zqi51Wu5qC0 Types of Statistics 1.Descriptive Descriptive statistics :Mean, mode, standard deviation, median ,absolute deviation, kurtosis, skewness 2.Inferential Types of data 1) Categorical (nomial,ordinal) 2) Numerical (discerte,continous) random variable(discerte random variable ,continous random variable) Quantile statistics Q1, Q2, Q3, min, max, range, interquartile range Central Limit Theorem,Bayes Theorem,Confidence Interval,Hypothesis Testing,z test, t test,f test,Confidence Interval,1 tail test, 2 tail test,chisquare test,anova test,A/B testing Categorical vs Categorical Chi-square test,Information gain,Cramer’s V Categorical vs Numerical Student T-test,ANOVA,Logistic regression,Discretize Y (left column),Point-biserial correlation Numerical vs Categorical Student T-test,ANOVA,Logistic regression,Discretize X (row above) Numerical vs Numerical Correlation,Linear Regression,Discretize Y (left column),Discretize X (row above) ***4.Feature selection*** https://github.com/solegalli/feature-selection-for-machine-learning upgini Free automated data enrichment library for machine learning https://github.com/upgini/upgini https://upgini.com/ FeatureSelector https://github.com/WillKoehrsen/feature-selector feature_engine https://github.com/solegalli/feature_engine 1.Filter methods (Removing Constant feature,Removing Quasi constant feature,Removing Duplication feature,Removing Correlated Features,feature importance,chisquare test,Ttest,ftest,vif,anova test,information gain,F-score,Mutual Information,hypothesis test,information gain,Univariate Selection Methods,SelectKBest,SelectPercentile,Variance threshold,Fisher’s Score,Dispersion ratio Mean Absolute Difference (MAD), constant features elimination, quasi-constant features elimination, duplicate feature elimination,univariate method,mutual information, correlation etc...),Correlation Coefficient,Variance Threshold ,Mean Absolute Difference (MAD),Dispersion ratio,Variance inflation,factor Condition Index 2.Wrapper methods (recursive feature eliminiation,Recursive feature addition,SelectKbest,boruta,mRMR,forward feature selection,backward feature elimination,Bi-directional selection,exhaustic feature selection,stepwise selection,step forward selection,step backward selection and exhaustive search etc...) 3.Embedded method (lasso regression,ridge regression,elastic net regression,tree based(Tree-based methods like Random Forest Importance etc...),Feature Selection by Tree importance,Feature selection with decision trees,regression coefficients(logistic,linear coeffiicients),Recursive feature elimination based on importance,Least absolute deviation) 4.Hybrid Method(Recursive Feature Selection,Recursive Feature addition,Recursive feature elimination,Feature Shuffling,Feature performance,Target mean performance,Permutation importance,Population stability index,Target encoding) unsupervised Feature selection:Principal Component Analysis,Independent Component Analysis,Non-Negative Matrix Factorization,t-distributed Stochastic Neighbor Embedding,Autoencoder Single-Agent Reinforcement Learning Feature Selection (SARLFS) ,Multi-Agent Reinforcement Learning Feature Selection (MARLFS) ITMO_FS is a feature selection library https://github.com/ctlab/ITMO_FS Sparse Features - Removing features,LASSO regularization,features dense(pca,Feature hashing),Using models that are robust to sparse features 5.Feature creation feature selection https://medium.com/analytics-vidhya/feature-selection-extended-overview-b58f1d524c1c mrmr_selection automatic feature selection at scale https://github.com/smazzanti/mrmr Feature selector https://github.com/WillKoehrsen/feature-selector Simulated Annealing https://github.com/kennethleungty/Simulated-Annealing-Feature-Selection boruta https://github.com/scikit-learn-contrib/boruta_py https://github.com/Ekeany/Boruta-Shap DropConstantFeatures DropDuplicateFeatures DropCorrelatedFeatures step forward feature selection https://www.kdnuggets.com/2018/06/step-forward-feature-selection-python.html automatic feature selection mrmr https://github.com/smazzanti/mrmr Creating New Features Deep Feature Synthesis https://docs.featuretools.com/en/v0.16.0/automated_feature_engineering/afe.html SequentialFeatureSelector: The popular forward and backward feature selection Alternative feature selection methods Feature shuffling,Feature performance,Target mean performance Automatic Feature Selection : recursive feature elimination and cross-validation Powershap: A Shapley feature selection method https://github.com/predict-idlab/powershap VarianceThreshold,Chi-squared stats,ANOVA using f_classif,Univariate Linear Regression Tests using f_regression,F-score vs Mutual Information,Mutual Information for discrete value,Mutual Information for continues value,SelectKBest,SelectPercentile,SelectFromModel,Recursive Feature Elimination,Extra Trees model 4.Feature Importance a.ExtraTreesClassifier,ExtraTreesregressor b.SelectKBest c.Logistic Regression d.Random_forest_importance,Permutation Feature Importance e.decision tree f.Linear Regression g.xgboost h.Pearson correlation Forward selection,Chi-square,Logit (Logistic Regression model) 5.curse of dimensionality (as dimension increases performance decreases) 6.highly correleated features then can take any 1 feature (multicollinearity) 7.dimension reduction 8.lasso regression to penalise unimportant features 9.VarianceThreshold ,selectkbest 10.model based selection 11.Mutual Information Feature Selection 12.remove features with very low variance (quasi constant feature dropping) 13.Univariate feature selection 14.importance of feature (random forest importance) 15.feature importance with decision trees 16.PyImpetus 17.drop constant features (variance=0) , Drop Highly Correlated Features 18.variance inflation factor(vif) 19.Recursive Feature Elimination RecursiveFeatureAddition 20.exchaustive feature selection 21.Statistical Methods , Hypothesis Testing ,Recursive Feature Elimination 22.Boruta https://github.com/scikit-learn-contrib/boruta_py https://analyticsindiamag.com/hands-on-guide-to-automated-feature-selection-using-boruta/ 23.Sequence Feature Selection, SelectFromModel Missing Value Ratio Analysis,Low Variance Filter,High Correlation Filter,Backward Feature Elimination,Forward Feature Elimination ,SequentialFeatureSelector PyImpetus https://github.com/atif-hassan/PyImpetus https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/ Automate your Feature Selection Workflow in one line of Python code https://github.com/AutoViML/featurewiz https://towardsdatascience.com/automate-your-feature-selection-workflow-in-one-line-of-python-code-3d4f23b7e2c4 https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/ https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/ https://www.analyticsvidhya.com/blog/2020/10/a-comprehensive-guide-to-feature-selection-using-wrapper-methods-in-python/ https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172 Feature Engineering Tools https://neptune.ai/blog/feature-engineering-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-feature-engineering-tools https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd PyRasgo https://github.com/rasgointelligence/PyRasgo https://docs.rasgoml.com/rasgo-docs/?_ga=2.209281745.2123722956.1645542654-525286113.1645542654 Automated Feature Engineering Using Deep Feature Synthesis (DFS) https://heartbeat.comet.ml/introduction-to-automated-feature-engineering-using-deep-feature-synthesis-dfs-3feb69a7c00b Automatic Feature Selection in python https://verstack.readthedocs.io/en/latest/#featureselector rulefit https://github.com/christophM/rulefit Featurewiz: Fast way to select the best features in a data select best features featurewiz https://github.com/AutoViML/featurewiz Featuretools: https://github.com/alteryx/featuretools https://analyticsindiamag.com/introduction-to-featuretools-a-python-framework-for-automated-feature-engineering/ AutoFeat: https://github.com/cod3licious/autofeat TSFresh: https://github.com/blue-yonder/tsfresh FeatureSelector: https://github.com/WillKoehrsen/feature-selector unsupervised feature selection technique https://github.com/atif-hassan/FRUFS rulefit https://github.com/christophM/rulefit ***5.Data splitting*** Splitting ratio of data deponds on size of dataset available Training data,Validation data,Testing data ***6.Model selection*** Machine learning https://scikit-learn.org/stable/index.html Choose the Right Machine Learning Algorithm for Your Application https://towardsdatascience.com/how-to-choose-the-right-machine-learning-algorithm-for-your-application-1e36c32400b9 Time Complexity Of Machine Learning Models -https://www.thekerneltrip.com/machine/learning/computational-complexity-learning-algorithms/ interactive tools https://github.com/Machine-Learning-Tokyo/Interactive_Tools mindsdb In-Database Machine Learning https://github.com/mindsdb/mindsdb HTML tables into Google Sheets -https://towardsdatascience.com/import-html-tables-into-google-sheets-effortlessly-f471eae58ac9 Machine Learning Playground https://ml-playground.com/ visual introduction to machine learning http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ draw a dataset from inside jupyter https://pypi.org/project/drawdata/ https://www.youtube.com/watch?v=b0rsDPQ3bjg Visual programming language for machine learning - Kobra https://kobra.dev/ compose generate labels for supervised learning https://github.com/alteryx/compose https://analyticsindiamag.com/guide-to-prediction-engineering-with-compose/ human-learn https://towardsdatascience.com/human-learn-create-rules-by-drawing-on-the-dataset-bcbca229f00 Neural Network https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.46672&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false Microscope https://microscope.openai.com/models https://www.youtube.com/watch?v=y0-ISRhL4Ks Ptpython Autocompletion, Autosuggestion, Docstring https://github.com/prompt-toolkit/ptpython https://towardsdatascience.com/ptpython-a-better-python-repl-6e21df1eb648 3 Tools to Track and Visualize the Execution of your Python Code https://towardsdatascience.com/3-tools-to-track-and-visualize-the-execution-of-your-python-code-666a153e435e ML Code memory Consuming https://towardsdatascience.com/how-much-memory-is-your-ml-code-consuming-98df64074c8f PyGrid Privacy-preserving, Decentralized Data Science https://github.com/OpenMined/PyGrid/ Best and Worst Cases of Machine-Learning Models https://medium.com/towards-artificial-intelligence/best-and-worst-cases-of-machine-learning-models-part-1-36cdb9296611 https://www.youtube.com/watch?v=mlumJPFvooQ&list=PLZoTAELRMXVM0zN0cgJrfT6TK2ypCpQdY skater Machine Learning Model Interpretation https://towardsdatascience.com/machine-learning-model-interpretation-47b4bc29d17f Speedml Speeding up Machine Learning https://towardsdatascience.com/speedml-speeding-up-machine-learning-5dccbf21effd 2-2000x faster ML algos https://github.com/danielhanchen/hyperlearn snapml 30 Times Faster Than Scikit-Learn snapml https://www.zurich.ibm.com/snapml/ scikit-learn-intelex https://github.com/intel/scikit-learn-intelex composer speed-up algorithms for model training https://github.com/mosaicml/composer pdpipe https://github.com/pdpipe/pdpipe pipeline https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html PHOTONAI A high level Python API for designing and optimizing machine learning pipelines https://www.photon-ai.com/ Machine Learning in Tableau with PyCaret https://towardsdatascience.com/machine-learning-in-tableau-with-pycaret-166ffac9b22e TabNet balances explainability and model performance on tabular data https://towardsdatascience.com/tabnet-e1b979907694 FreaAI That Automatically Finds Weaknesses In ML models https://analyticsindiamag.com/ibm-launches-freaai-that-automatically-finds-weaknesses-in-ml-models/ A.Supervised learning (have label data) Transformers for Tabular Data: TabTransformer https://github.com/lucidrains/tab-transformer-pytorch 1.Regression (output feature in continous data form) linear regression,Multiple Linear Regression,polynomial regression,Exponential Regression,Bayesian Regression,Robust Regression,Huber regressor,support vector regression,Decision Tree Regression,Random Forest Regression,TensorFlow Decision Forests,RANSAC Regression, least square method,linear-tree,Random Forest Regression, Regularized Greedy Forests,xgboost,ridge(L2 Regularization),lasso(L1 Regularization (more sparse)),elastic, Lars,catboost,gradientboosting,adaboost,Explainable Boosting Machine,Histogram-Based Gradient Boost,Stacked Gradient Boosting Machines,LightBoost,CatBoost, XGBoost,autoxgb,NGBoost,XBNet,Chefboost,GPBoost,Local Cascade Ensemble,Principal Component Regression,huber_regression,ransac_regression,theilsen_regression,Linear spline,Isotonic regression,Bin regression,Cubic spline,Natural cubic splin,Exponential moving average,Quantile Regression,Quantile Random Forests,Quantile GBM elsatic net,light gbm,ordinary least squares,cart,Stepwise Regression,Multivariate Adaptive Regression Splines ,Generalised Additive Model(learn non-linear feature),tabnet,Linear Tree regression statsassume Automating Assumption Checks for Regression Models https://github.com/kennethleungty/statsassume Locally Weighted Linear Regression https://towardsdatascience.com/locally-weighted-linear-regression-in-python-3d324108efbf TuringBot https://www.youtube.com/watch?v=LyKzKvjyIPo chefboost is an alternative library for training tree-based models https://github.com/serengil/chefboost growtrees About Cost-Aware Robust Tree Ensembles for Security Applications https://github.com/surrealyz/growtrees 2.Classification (output feature in categorical data form) Binary,Multi-class,Multi-labe Logistic Regression,K-Nearest Neighbors,Support Vector Machine,Kernel SVM,Naive Bayes,Decision Tree Classification,linear-tree,TensorFlow Decision Forests, Random Forest Classification,TensorFlow Decision Forests, Regularized Greedy Forests,xgboost,DART booster,autoxgb,LightGBM,adaboost,Gradient Boost,XBNet,catboost,gaussian NB,LGBMClassifier,LinearDiscriminantAnalysis, Extreme Gradient Boosting Machine, Explainable Boosting Machine,fairgbm ,Chefboost,GPBoost,NGBoost,Local Cascade Ensemble,passive aggressive classifier algorithm,cart,c4.5,c5.0,tabnet,ExtraTreesClassifier,TabPFN https://mlwhiz.com/blog/2019/11/12/dtsplits/?utm_campaign=the-simple-math-behind-3-decision-tree-splitting-criterions&utm_medium=social_link&utm_source=missinglettr-linkedin 4 Useful techniques avoid overfitting in decision trees https://towardsdatascience.com/4-useful-techniques-that-can-mitigate-overfitting-in-decision-trees-87380098bd3c Machine Learning – it’s all about assumptions https://www.kdnuggets.com/2021/02/machine-learning-assumptions.html GPBoost: A Library To Combine Tree-Boosting With Gaussian Process And Mixed-Effects Models https://analyticsindiamag.com/guide-to-gpboost-a-machine-learning-library-to-combine-tree-boosting/ Data and Concept Drift https://evidentlyai.com/blog/machine-learning-monitoring-data-and-concept-drift B.Unsupervised learning(no label(target) data) 1.Dimensionality reduction - PCA,ppa,SVD,LDA,som,tsne,openTSNE,plsr,pcr,autoencoders,kernelpca,Latent Semantic Analysis,Factor Analysis,Locality Preserving Projections,Isometric Mapping,Multiple correspondence analysis (MCA),Multiple factor analysis (MFA),Factor analysis of mixed data (FAMD),vae,CompressionVAE,Gaussian Mixture Model,Bayesian Gaussian Mixture Model non-linear data using Kernel PCA, Non-Negative Matrix Factorization(NMF), IsoMap, t-SNE, and UMAP,TDA(Topological Data Analysis) t-SNE Effectively https://distill.pub/2016/misread-tsne/ 2.Clustering : Centroid-based Model ,Density-based Model ,Distribution-based Model,Connectivity-based model 17 clustering https://towardsdatascience.com/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a https://neptune.ai/blog/clustering-algorithms?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-clustering-algorithms classix Fast and explainable clustering based on sorting https://github.com/nla-group/classix https://www.mygreatlearning.com/blog/unsupervised-machine-learning/?highlight=unsupervised%20machine%20learning&utm_source=GLA&utm_medium=Blog&utm_campaign=1-16th%20May https://scikit-learn.org/stable/modules/clustering.html https://machinelearningmastery.com/clustering-algorithms-with-python/ https://towardsdatascience.com/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a RFM Segmentation in E-Commerce https://towardsdatascience.com/rfm-segmentation-in-e-commerce-e0209ce8fcf6 kmodes https://www.youtube.com/watch?v=8eATPLDJ0NQ Agglomerative Hierarchical Clustering Using AGNES Algorithm https://analyticsindiamag.com/perform-agglomerative-hierarchical-clustering-using-agnes-algorithm/ CLARANS Clustering Algorithm https://analyticsindiamag.com/comprehensive-guide-to-clarans-clustering-algorithm/ https://pub.towardsai.net/fully-explained-birch-clustering-for-outliers-with-python-2ad6243f126b https://www.kdnuggets.com/2020/12/algorithms-explained-k-means-k-medoids-clustering.html https://www.kdnuggets.com/2017/03/naive-sharding-centroid-initialization-method.html CLASSIX clustering https://github.com/nla-group/classix K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines https://www.kdnuggets.com/2021/01/k-means-faster-lower-error-scikit-learn.html#.YAHAAIpnx4A.linkedin k-Means Clustering by up to 10x Over Scikit-Learn https://towardsdatascience.com/how-to-speed-up-your-k-means-clustering-by-up-to-10x-over-scikit-learn-5aec980ebb72 3.Association Rule Learning - support,lift,confidence,leverage,Conviction,aprior,elcat,Fp-growth,Fp-tree construction,FP-Max Algorithm,association_rules,Frequent Itemset Mining,Multi-Relation Association Rules,High-order pattern discovery,K-optimal pattern discovery,Approximate Frequent Itemset,Generalized Association Rules,Quantitative Association Rules,Interval Data Association Rules,Sequential pattern mining,Hypergeometric Networks,Constraint Based Mining,Multi-level Association Rules,Fuzzy Association Rules Sequential Patterns Generalized Sequential Patterns (GSP) Prefix-Projected Sequential Pattern Mining (PrefixSpan) Sequential Pattern Discovery using Equivalent Class (SPADE) Frequent Pattern-Projected Sequential Pattern Mining (FreeSpan) interpretable association rule https://analyticsindiamag.com/a-guide-to-interpretable-association-rule-mining-using-pycaret/ 4.Market Segmentation Demographic Segmentation,Geographic segmentation,Firmographic segmentation,Behavioural segmentation, 4.Recommendation system - Surprise,TensorFlow Recommendation,Recmterics competitive-recsys https://github.com/chihming/competitive-recsys a.collaborative Recommendation system (model based, memory based(item based,user based),hybrid) user-item interaction matrix Classification-based collaborative filtering Model-based collaborative filtering systems(Cluster model,linear regression,Bayesian networks ,latent factor(probabilistic latent,matrix factorization(als,SGD,SVD),neural network,lda)) b.content based Recommendation system similarity based(user-user similarity,item-item similarity) matrix factorization(SVD and SVD++),Popularity-based recommenders c.utility based Recommendation system d.knowledge based Recommendation system e.demographic based Recommendation system f.hybrid based Recommendation system Popularity based Recommendation system (NON-PERSONALIZED ) g.Average Weighted Recommendation h.using K Nearest Neighbor i.cosine distance recommender system item2vec j.TensorFlow Recommenders https://www.tensorflow.org/recommenders recommenders https://github.com/microsoft/recommenders Neural Collaborative Filtering for Personalized Ranking AutoRec: Rating Prediction with Autoencoders Matrix Factorization k.suprise baseline model Context-aware Recommender Systems,Mobile Recommender Systems,Group Recommender Systems,Multi-stakeholder Recommender Systems l.Neural Collaborative Filtering (NCF) l.Tf-Rec TensorFlow Recommendation https://github.com/Praful932/Tf-Rec Nvidia Merlin m.Deep Learning Recommendation Models https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html Restricted Boltzmann Machines,Auto-Encoders TOROS Buffalo https://github.com/kakao/buffalo recommenders-https://github.com/microsoft/recommenders LightFM https://making.lyst.com/lightfm/docs/home.html lkpy Python recommendation toolkit https://github.com/lenskit/lkpy https://analyticsindiamag.com/how-to-build-recommender-systems-using-lenskit/ torchrec https://github.com/pytorch/torchrec PyTorch implementations of deep reinforcement learning algorithms and environments https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch recmetrics library of metrics for evaluating recommender systems https://github.com/statisticianinstilettos/recmetrics Downsize Recommendation Models By 112 Times https://analyticsindiamag.com/explained-facebooks-novel-method-to-downsize-recommendation-models-by-112-times/ torchrec,Lenskit,RGRecSys,Surprise,Tensorflow Recommenders,NVIDIA-Merlin,Recmetrics,Surprise,DeepCTR,OpenRec,fastFM,LightFM Session-based RecSys could be done with:Recency-based Weighting (exp.decay),Probabilistic Graphical Models (FPMC, FOSSIL),Convolutional NN (Caser, NextItNet),Recurrent NN (GRU4Rec),Graph NN (SRGNN, GCSAN),Attention(STAMP, NARM, FDSA, SHAN),Transformer(BERT4Rec, Transformer4Rec),Knowledge Graph(KSR, GRU4RecKG, KGCN, KGAT, RippleNet),Landscape, Rexy, Tensor Recommendation Engine, Light FM, Spotlight, Case Recommender https://analyticsindiamag.com/top-open-source-recommender-systems-in-python-for-your-ml-project/ https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8 https://machinelearningmastery.com/recommender-systems-resources/ C.Ensemble methods 1.Stacking models https://www.analyticsvidhya.com/blog/2021/03/advanced-ensemble-learning-technique-stacking-and-its-variants/? vecstack https://github.com/vecxoz/vecstack Cascading Ensembles,Cohorted Ensembles 2.Bagging models (Bagging (with the replacement) , Pasting ( without replacement )) 3.Boosting models 4.Blending 5.Voting (Hard Voting,Soft Voting) VOTING ENSEMBLE Simple : Max Voting, Averaging, Weighted Averaging,Simple Average,Rank Averaging,Bayesian Model,Majority Voting mlens ML-Ensemble – high performance ensemble learning https://github.com/flennerhag/mlens https://analyticsindiamag.com/do-ensemble-methods-always-work/ Shapley value of players (models) in weighted voting games https://github.com/benedekrozemberczki/shapley D.Reinforcement learning https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses 2 types a)model free b)model based gym-https://github.com/openai/gym reinforcement learning by using PyTorch-https://github.com/SforAiDl/genrl agent,environment,policy(On-Policy vs Off-Policy),reward function,value function,state,action,episode,actor-critic agent apply action to environment get corresponding reward so that it learn environment How to get started with Reinforcement Learning https://gordicaleksa.medium.com/how-to-get-started-with-reinforcement-learning-rl-4922fafeaf8c 1.Q-Learning 2.Deep Q-Learning 3.Deep Convolutional Q-Learning Deep Deterministic Policy Gradient 4.Twin Delayed DDPG,DQN,Temporal difference 5.A3C (Actor Critic) ,A2C, Soft Actor Critic (SAC),Adversarial Motion Priors (AMP),Cross-Entropy Method (CEM),Deep Deterministic Policy Gradient (DDPG),Double Deep Q-Network (DDQN),Deep Q-Network (DQN),Proximal Policy Optimization (PPO),Q-learning (Q-learning),Soft Actor-Critic (SAC),State Action Reward State Action (SARSA),Twin-Delayed DDPG (TD3),Trust Region Policy Optimization (TRPO) 6.Advantage weighted actor critic (AWAC). 7.XCS 8.genetic algorithm,sarsa,natural policy gradient,Policy Gradient Learning https://simoninithomas.github.io/deep-rl-course/ SARSA,REINFORCE,PPO,DDPG,Ddpg,TD3 AUTORL: AUTOML FOR RL https://www.automl.org/blog-autorl/ Environments-OpenAI Gym, DeepMind Lab, Unity ML-Agents https://data-flair.training/news/python-libraries-for-reinforcement-learning/ https://analyticsindiamag.com/8-best-free-resources-to-learn-deep-reinforcement-learning-using-tensorflow/ https://analyticsindiamag.com/top-8-autonomous-driving-open-source-projects-one-must-try-hands-on/ https://analyticsindiamag.com/8-toolkits-for-reinforcement-learning-models-that-make-reasoning-explainability-core-to-ai/ https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses https://towardsdatascience.com/value-based-methods-in-deep-reinforcement-learning-d40ca1086e1 https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-reinforcement-learning-tutorials-examples-projects-and-courses TensorForce: A TensorFlow-based Reinforcement Learning Framework https://analyticsindiamag.com/guide-to-tensorforce-a-tensorflow-based-reinforcement-learning-framework/ Decision Transformer: Reinforcement Learning via Sequence Modeling https://github.com/kzl/decision-transformer Open AI Gym - https://gym.openai.com/ DeepMind’s MuZero https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?utm_campaign=Learning%20Posts&utm_content=150411901&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323 KerasRL https://github.com/keras-rl/keras-rl pyqlearning tensorforce https://tensorforce.readthedocs.io/en/latest/index.html Practical_RL https://github.com/yandexdataschool/Practical_RL rl_coach https://github.com/IntelLabs/coach#installation MushroomRL https://mushroomrl.readthedocs.io/en/latest/ TFAgents https://github.com/tensorflow/agents (https://www.tensorflow.org/agents) https://deepmind.com/blog/article/trfl TorchRec https://pytorch.org/blog/introducing-torchrec/ TensorFlow Recommenders https://www.tensorflow.org/recommenders behaviour trees used in reinforcement learning https://analyticsindiamag.com/how-are-behaviour-trees-used-in-reinforcement-learning/ Automate The Stock Market Using FinRL (Deep Reinforcement Learning Library) https://analyticsindiamag.com/stock-market-prediction-using-finrl/ Stable Baselines https://github.com/openai/baselines https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc https://neptune.ai/blog/the-best-tools-for-reinforcement-learning-in-python?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-the-best-tools-for-reinforcement-learning-in-python Semi-Supervised Learning-small amount of labeled data with a large amount of unlabeled data during training Machine Learning with Graphs http://web.stanford.edu/class/cs224w/ E.Deep-learning (use when have huge data and data is highly complex and state of art for unstructured data) https://www.kdnuggets.com/2019/11/designing-neural-networks.html Model Zoo Discover open source deep learning code and pretrained models https://modelzoo.co/ Visualizing your Neural Network with Netron,Net2Vis,visualkeras,draw_convnet,NNSVG,PlotNeuralNet,Tensorboard,Caffe,Matlab,Keras.js,keras-sequential-ascii ,Netron,DotNet,Graphviz ,Keras Visualization,Conx,ENNUI,NNet,GraphCore ,Monial,Quiver Sharing the best resources on various machine learning topics https://www.backprop.org/ deeplearning-models-https://github.com/rasbt/deeplearning-models Deep-Learning-with-PyTorch- https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf Frameworks:Pytorch,Tensorflow,Keras,caffe,theano,MXNet,Matlab,Microsoft Cognitive Toolkit,opacus(Train PyTorch models with Differential Privacy) https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464 https://docs.deepstack.cc/getting-started/index.html fastest way to build, debug, and interpret neural networks https://www.perceptilabs.com/ Nengo: A New Neural Network Building and Deployment Tool https://pub.towardsai.net/nengo-a-new-neural-network-building-and-deployment-tool-66677c65fa19 Binarized Neural Network memory size is reduced, and bitwise operations improve the power efficiency https://neptune.ai/blog/binarized-neural-network-bnn-and-its-implementation-in-ml paddlehub https://github.com/PaddlePaddle/PaddleHub Performing Computer Vision & NLP Tasks in a Single Of Code https://towardsdatascience.com/performing-computer-vision-nlp-tasks-in-a-single-of-code-f7205f212d34 scikit-neuralnetwork https://towardsdatascience.com/the-simplest-way-to-train-a-neural-network-in-python-17613fa97958 https://github.com/aigamedev/scikit-neuralnetwork NVIDIA’s Kaolin: A 3D Deep Learning Library https://analyticsindiamag.com/nvidias-kaolin-3d-deep-learning-library/ https://github.com/NVIDIAGameWorks/kaolin PySyft is a Python library for secure and private Deep Learning https://github.com/OpenMined/PySyft keras-vis Visualizing Learning of a Deep Neural Network https://towardsdatascience.com/deep-learning-model-visualization-6cf6290dc981 Deep Replay Visualizing Learning of a Deep Neural Network https://towardsdatascience.com/visualizing-learning-of-a-deep-neural-network-b05f1711651c keras-visualizer Visualizing Keras Models https://towardsdatascience.com/visualizing-keras-models-4d0063c8805e Lucid Library is an open source framework to improve the interpretation of deep neural networks Gradient-Centralization-TensorFlow improve your training performance of TensorFlow models with just 2 lines of code! https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow XBNet: An Extremely Boosted Neural Network MIL-WebDNN Fastest DNN Execution Framework on Web Browser https://mil-tokyo.github.io/webdnn/ Vector Hub models to turn data into vectors text2vec, image2vec, video2vec, graph2vec, bert, inception, etc https://github.com/RelevanceAI/vectorhub torchbearer: A model fitting library for PyTorch https://github.com/pytorchbearer/torchbearer 1.Multilayer perceptron(MLP) 1.Regression task 2.Classification task Tabnet and deep tables for tabular dataset using deep learning 2.Convolutional neural network ( use for image data) Best MLOps Tools for Your Computer Vision Project Pipeline https://neptune.ai/blog/best-mlops-tools-for-computer-vision-project?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-mlops-tools-for-computer-vision-project mediapipe https://google.github.io/mediapipe/ cv modelhub https://modelplace.ai/ all openmmlab https://github.com/open-mmlab mmdetection,mmsegmentation,mmediting,mmdetection3d,mmaction2,mmocr,mmpose,etc... glasses High-quality Neural Networks for Computer Vision https://github.com/FrancescoSaverioZuppichini/glasses IceVision https://airctic.com/0.8.0/ Top Computer Vision Google Colab Notebooks- https://www.qblocks.cloud/creators/computer-vision-google-colab-notebooks for low code object detection (detecto)- https://github.com/alankbi/detecto CV-pretrained-model- https://github.com/balavenkatesh3322/CV-pretrained-modelCV-pretrained-model- Fast Computer Vision Model Building PyTorch Lightning Flash and FiftyOne https://towardsdatascience.com/open-source-tools-for-fast-computer-vision-model-building-b39755aab490 5 Open-Source Facial Recognition https://medium.com/analytics-vidhya/ways-to-boost-your-computer-vision-projects-by-using-5-open-source-facial-recognition-projects-56668f170cb9 cnn alternative CapsNet https://github.com/XifengGuo/CapsNet-Keras EDA for image data data-gradients 1.Classification of image albumentations https://github.com/albumentations-team/albumentations AugLy https://github.com/facebookresearch/AugLy create own model,Lenet,Alexnet,DenseNet,MobileNet,ShuffleNet,SqueezeNet,Resenet,GoogleNet,Inception,Vgg16,vgg19,Efficient,EfficientNetV2,EfficientDet,residualnet,Nasnet,STN,nasneta,senet,amoebanetc,DeiT (tiny,small,base),Meta Pseudo Labels,res-mlp-pytorch,MLP-Mixer,vit,DynamicViT, FNet,gMLP models,nfnet mmclassification https://github.com/open-mmlab/mmclassification https://theaisummer.com/cnn-architectures/ https://paperswithcode.com/sota/image-classification-on-imagenet timm https://pypi.org/project/timm/ https://github.com/rwightman/pytorch-image-models 2.Localization of object in image 3.Object detection and object segmentation rcnn,fastrcnn,fastercnn,TensorFlow Object Detection,yolo v1,yolo v2,yolo v3,SlimYOLOv3,yolo v4,PP-YOLO,scaled yolov4,YOLOR,YoloV5,YOLOS,efficinetdet,fast yolo,yolo tiny,yolo lite,yolo tiny++,yolo act++,yolonas,yolov8 maskrcnn,DeepLab-v3-plus,ssd,detectron,detectron2,D2Go,mobilenet,retinanet,R-fcn,Libra_R-CNN,detr facebook,mdetr,pspnet,segnet,U-net,UNet++,Efficient U-Nets, 𝗗𝗲𝗻𝘀𝗲-𝗚𝗮𝘁𝗲𝗱 𝗨-𝗡𝗲𝘁, nnU-Net,v-net,TransUNet, H-DenseUNet, MultiResUNet ,deeplab,globalconvolutionnetwork,fcn,EfficientDet,Vision Transformer,deit,VarifocalNet (VF-Net),DINO,BodyPix,vit,AugFPN,mlsd PixelLib Simplifying Object Segmentation with PixelLib Library https://github.com/ayoolaolafenwa/PixelLib mmdetection https://github.com/open-mmlab/mmdetection https://towardsdatascience.com/mmdetection-tutorial-an-end2end-state-of-the-art-object-detection-library-59064deeada3 https://github.com/open-mmlab/mmrotate mmdetection3d https://github.com/open-mmlab/mmdetection3d mmsegmentation https://github.com/open-mmlab/mmsegmentation fewshot https://github.com/open-mmlab/mmfewshot Zero-Shot Object Detection , annotate dataset https://github.com/microsoft/GLIP imageai.Detection ObjectDetection Segmentation models https://github.com/qubvel/segmentation_models Image-Segmentation-Using-Pixellib IceVision https://airctic.com/0.8.0/ Image Generation Using TensorFlow Keras https://analyticsindiamag.com/getting-started-image-generation-tensorflow-keras/ Video Understanding https://towardsdatascience.com/video-understanding-made-simple-with-pytorch-video-and-lightning-flash-c7d65583c37e Getting Started With Object Detection Using TensorFlow https://analyticsindiamag.com/object-detection-using-tensorflow/ Instance Segmentation using Mask-RCNN with PixelLib and Python https://www.youtube.com/watch?v=i_-ud01wFhc MLP MLP solution for Vision, from Google AI https://github.com/lucidrains/mlp-mixer-pytorch MMDetection https://analyticsindiamag.com/guide-to-mmdetection-an-object-detection-python-toolbox/ mediapipe https://github.com/google/mediapipe SSL Framework For Object Detection https://analyticsindiamag.com/googles-stac-ssl-framework-for-object-detection/ GSDT https://analyticsindiamag.com/gsdt-gnns-for-simultaneous-detection-and-tracking/ D2Go Brings Detectron2 To Mobile https://analyticsindiamag.com/facebooks-d2go-brings-detectron2-to-mobile/ AdelaiDet open source toolbox for multiple instance-level detection and recognition tasks https://github.com/aim-uofa/AdelaiDet 3d object detection https://omdena.com/blog/3d-object-detection/?utm_source=linkedin&utm_medium=organic&utm_campaign=blog&utm_term=google-analytics PyMAF https://analyticsindiamag.com/guide-to-pymaf-pyramidal-mesh-alignment-feedback/ 3 kind of object segmentation are available semantic segmentation,instance segmentation,panoptic segmentation segmentation_models https://github.com/qubvel/segmentation_models https://analyticsindiamag.com/guide-to-panoptic-segmentation-a-semantic-instance-segmentation-approach/ https://analyticsindiamag.com/semantic-vs-instance-vs-panoptic-which-image-segmentation-technique-to-choose/ ResNeSt: A Better ResNet with the Same Costs https://analyticsindiamag.com/guide-to-resnest-a-better-resnet-with-the-same-costs/ PAN: Pyramid Attention Network for Semantic Segmentation https://medium.com/mlearning-ai/review-pan-pyramid-attention-network-for-semantic-segmentation-semantic-segmentation-8d94101ba24a PyTorch based low code object detection-https://github.com/alankbi/detecto https://www.kdnuggets.com/2021/03/extraction-objects-images-videos-5-lines-code.html autogluon GluonCV https://medium.com/apache-mxnet/start-fitting-cv-models-like-scikit-learn-with-gluoncv-0-10-931ff910a38 https://awesomeopensource.com/project/hoya012/deep_learning_object_detection 4.objecttracking (mean shit and optical flow and kalman filter) Tracktor++,Trackrcnn,Jde,DeepSORT,FairMOT mmtracking https://github.com/open-mmlab/mmtracking https://github.com/open-mmlab/mmflow mmhuman3d https://github.com/open-mmlab/mmhuman3d Video Understanding https://github.com/open-mmlab/mmaction2 5.Deepdream,Neural style transfer, Pose estimation generative models https://github.com/open-mmlab/mmgeneration Machine Learning for Art https://ml4a.net/# Pose estimation by mediapipe library https://google.github.io/mediapipe/ https://www.youtube.com/watch?v=brwgBf6VB0I posemodule https://www.youtube.com/watch?v=5kaX3ta398w Pose Tracking https://www.youtube.com/watch?v=0JU3kpYytuQ&t=1650s 6.DEEP LEARNING METHODS FOR 2D :OpenPose,DeepPose,AlphaPose,tfpose,MultiPoseNet,AlphaPose,Movenet lighting,VIBE,DeeperCut,Mask RCNN,DeepCut,Convolutional Pose Machines,PoseNet,MoveNet,Adobe’s BodyNet,MoveNet and TensorFlow.js,High-Resolution Net,Blaze pose,Deep Pose,PoseNet openpose wrnchai densepose mmpose https://github.com/open-mmlab/mmpose Pose Estimation using OpenCV https://www.analyticsvidhya.com/blog/2021/05/pose-estimation-using-opencv/ https://medium.com/beyondminds/an-overview-of-human-pose-estimation-with-deep-learning-d49eb656739b 3D POSE ESTIMATION 3D Image Classification https://keras.io/examples/vision/3D_image_classification/ TensorFlow 2 Object Detection API tutorial https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/ https://blog.paperspace.com/how-to-train-scaled-yolov4-object-detection/ Image DA libraries – Augmentor, Albumentations, ImgAug, AutoAugment, Transforms https://neptune.ai/blog/data-augmentation-in-python Simple transformations-Resize,Gray Scale,Normalize,Random Rotation,Center Crop,Random Crop,Gaussian Blur Position augmentation-Scaling,Cropping,Flipping,Padding,Rotation,Translation,Affine transformation,Kernel filters Color augmentation-Brightness,Contrast,Saturation,Hue Deep learning approach-Adverserial training,Neural style transfer,Gan data argumentation AS-One Run YOLOv7,v6,v5,R,X in under 20 lines of code https://github.com/augmentedstartups/AS-One Data augmentation feature space : noise,interpolation Data Space Character Level : Noise Induction,Rule-based Transformations Word Level : Noise Induction,Synonym Replacement,Embedding Replacement,Replacement by Language Models Phrase and Sentence Level : Interpolation,Structure-based Transformation Document Level:Round-trip Translation,Generative Methods flipping, rotation, scaling ratio, noise injection, changing contrast, translation, cropping, color jittering,AutoAugment,Fast AutoAugment,Population Based Augmentation,RandAugment More advanced techniques-Gaussian Noise,Random Blocks,Central Region albumentations https://github.com/albumentations-team/albumentations https://towardsdatascience.com/getting-started-with-albumentation-winning-deep-learning-image-augmentation-technique-in-pytorch-47aaba0ee3f8 AugLy A Modern Data Augmentation Library https://analyticsindiamag.com/complete-guide-to-augly-a-modern-data-augmentation-library/ https://github.com/facebookresearch/AugLy Data augmentation with tf.data ImageGenerator image augmentation ImageDataGenerator Albumentations SOLT Imgaug Augmentor,Albumentations,Imgaug,AutoAugment (DeepAugment) Augmentor Image augmentation library in Python for machine learning https://github.com/mdbloice/Augmentor albumentations https://github.com/albumentations-team/albumentations HiSD: Image-to-Image translation via Hierarchical Style Disentanglement https://analyticsindiamag.com/hisd-python-implementation-of-image-to-image-translation/ Zooming Slow-Mo https://analyticsindiamag.com/guide-to-zooming-slow-mo-one-stage-space-time-video-super-resolution/ Image Augmentation Pipelines with Tensorflow https://towardsai.net/p/machine-learning/building-complex-image-augmentation-pipelines-with-tensorflow-bed1914278d2 TensorFlow2.0-Examples https://github.com/YunYang1994/TensorFlow2.0-Examples unadversarial https://github.com/microsoft/unadversarial/ https://analyticsindiamag.com/microsoft-research-unadversarial/ CNNs 'see' - FilterVisualizations, Heatmaps,Saliency Maps,saliency_map_guided,Heat Map Visualizations,GradCAM,Class Activation Maps,ZFNet,Lucid,Activation Atlas,Blur Integrated Gradients,concept whitening,Integrated Gradients,SmoothGrad,PytorchRevelio,Feature Visualizer, Guided Gradients, grad_cam,sensitivity_analysis,Captum,Preliminary Methods,Plot Model Architecture,Visualize Filters,Activation based Methods,Maximal Activation,Image Occlusion,Gradient based Methods,Gradient based Class Activation Map Tools to Design or Visualize Architecture of Neural Network https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network quiver Interactive convnet features visualization for Keras https://github.com/keplr-io/quiver https://jair-neto.medium.com/a-powerful-method-for-explainability-of-object-detection-algorithms-ace0fe4623e7 https://github.com/utkuozbulak/pytorch-cnn-visualizations https://microscope.openai.com/models https://github.com/balavenkatesh3322/CV-pretrained-model Mediapipe for Python https://google.github.io/mediapipe/ imageai.Detection for Object detection cnn-raccoon interactive dashboards for your Convolutional Neural Networks with a single line of code https://github.com/lucko515/cnn-raccoon deit https://github.com/facebookresearch/deit https://wandb.ai/thibault-neveu/detr-tensorflow-log/reports/Finetuning-DETR-Object-Detection-with-Transformers-on-Tensorflow-A-step-by-step-tutorial--VmlldzozOTYyNzQ https://github.com/Visual-Behavior/detr-tensorflow awesome-computer-vision-models https://github.com/nerox8664/awesome-computer-vision-models EfficientDet https://github.com/ravi02512/efficientdet-keras Vision Transformer - Pytorch https://github.com/lucidrains/vit-pytorch https://github.com/alohays/awesome-visual-representation-learning-with-transformers T2T-ViT https://analyticsindiamag.com/complete-guide-to-t2t-vit-training-vision-transformers-efficiently-with-minimal-data/ https://github.com/yitu-opensource/T2T-ViT Explainability for Vision Transformers https://github.com/jacobgil/vit-explain https://keras.io/examples/vision/image_classification_with_vision_transformer/ https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples https://github.com/google-research/vision_transformer DeepLab-v3-plus Semantic Segmentation in TensorFlow https://github.com/rishizek/tensorflow-deeplab-v3-plus DEEP LEARNING METHODS FOR 3D:3D human pose estimation= 2D pose estimation + matching,Integral Human Pose Regression,Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach,A Simple Yet Effective Baseline for 3d Human Pose Estimation, Data Augmentation apply to increase size of dataset and performance of model low code object detection - detecto https://github.com/alankbi/detecto AutoML https://github.com/dataloop-ai/AutoML Object Detection with 10 lines of code-https://www.datasciencecentral.com/profiles/blogs/object-detection-with-10-lines-of-code https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606 Detecto https://github.com/alankbi/detecto https://medium.com/analytics-vidhya/computer-vision-in-healthcare-detection-of-fractures-3313fe6452fc OneNet-https://analyticsindiamag.com/onenet/ Norfair https://github.com/tryolabs/norfair Remo Improves Image Management https://www.freecodecamp.org/news/manage-computer-vision-datasets-in-python-with-remo/ yolo https://github.com/zzh8829/yolov3-tf2 https://github.com/ultralytics/yolov5 https://github.com/ashishpatel26/Yolov5-King-of-object-Detection https://github.com/sicara/tf2-yolov4 clip https://github.com/openai/CLIP bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN https://analyticsindiamag.com/a-beginners-guide-to-bayesian-cnn/ 3.Recurrent neural network (use when series of data) 1.RNN 2.GRU 3.LSTM (have memory cell,forget gate etc..) Depth Gated RNNs,Peephole connection,Coupled Input and Forget,Clockwork RNNs,RNN Initialized Using Identity Matrix(IRNN) 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 better than LSTM/GRU https://github.com/ashishpatel26/tcn-keras-Examples 4.Information Discrimination Units (IDU) https://github.com/hjeun/idu Train an LSTM Model ~30x Faster Using PyTorch with GPU https://towardsdatascience.com/how-to-train-an-lstm-model-30x-faster-using-pytorch-with-gpu-e6bcd3134c86 all above 3 models have bidirectional also based on problem statement use bidirectional models Quasi-Recurrent Neural Network https://github.com/salesforce/pytorch-qrnn textgenrnn https://github.com/minimaxir/textgenrnn 4.Generative adversarial network https://poloclub.github.io/ganlab/ https://developers.google.com/machine-learning/gan/training gan lab https://poloclub.github.io/ganlab/ https://neptune.ai/blog/generative-adversarial-networks-gan-applications?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-generative-adversarial-networks-gan-applications Diffusion Models Beat GANs on Image Synthesis https://paperswithcode.com/paper/diffusion-models-beat-gans-on-image-synthesis?from=n9 MUNIT: Multimodal Unsupervised Image-to-Image Translation (GAN) https://jonathan-hui.medium.com/gan-gan-series-2d279f906e7b generative adversarial transformers https://github.com/dorarad/gansformer LipGAN https://github.com/Rudrabha/LipGAN Wav2Lip https://github.com/Rudrabha/Wav2Lip BigGAN https://analyticsindiamag.com/hands-on-guide-to-biggan-with-python-code/ Cycle gan,Big GAN Style GAN,Dcgan,cGAN,SRGAN,InfoGAN,stargan,attan gan,stylegan,,PixelRNN,StackGAN,DiscoGAN,lsGAN,Conditional GAN(Pix2Pix),Progressive GANs( produces higher resolution images,Image-to-Image Translation),Face Inpainting,Super-resolution,Progressive Growing GAN,Instance-Conditioned GAN,Wasserstein GAN(improve image generation),ChromaGan,GANsformers,Conditional GAN and Unconditional GAN,Least Square GAN,Auxilary Classifier GAN,Dual Video Discriminator GAN,SRGAN,StackGAN,CycleGAN,WGAN diffusion https://github.com/openai/guided-diffusion https://www.analyticsvidhya.com/blog/2021/05/progressive-growing-gan-progan/ 5 Alternatives To Deep Nostalgia https://analyticsindiamag.com/top-5-alternatives-to-deep-nostalgia/ MixNMatch https://github.com/Yuheng-Li/MixNMatch Quantum GAN https://analyticsindiamag.com/now-gans-are-being-used-for-drug-discovery-complete-guide-to-quantum-gan-with-python-code/ https://analyticsindiamag.com/guide-to-differentiable-augmentation-for-data-efficient-gan-training/ https://analyticsindiamag.com/hands-on-python-guide-to-style-based-age-manipulation-sam-technique/ Imaginaire https://analyticsindiamag.com/guide-to-nvidia-imaginaire-gan-library-in-python/ Disentanglement https://analyticsindiamag.com/what-is-face-identity-disentanglement-and-how-it-outperformed-gans/ StyleFlow https://github.com/RameenAbdal/StyleFlow https://github.com/hindupuravinash/the-gan-zoo https://analyticsindiamag.com/top-10-tools-for-generative-adversarial-networks/ 5.Autoencoder 1.sparse Autoencoder 2.denoising Autoencoder 3.Contractive Autoencoder 4.stacked Autoencoder 5.deep Autoencoder 6.variational autoencoder 7.convolutional autoencoder Beta Variational Autoencoder,VAE with Linear Normalizing Flows ,VAE with Inverse Autoregressive Flows ,Disentangled Beta Variational Autoencoder,Disentangling by Factorising (FactorVAE),Beta-TC-VAE (BetaTCVAE),Importance Weighted Autoencoder (IWAE),VAE with perceptual metric similarity,Wasserstein Autoencoder (WAE),Info Variational Autoencoder,VAMP Autoencoder (VAMP),Hyperspherical VAE (SVAE),Adversarial Autoencoder (Adversarial_AE),Variational Autoencoder GAN (VAEGAN) ,Vector Quantized VAE (VQVAE),Hamiltonian VAE (HVAE),Regularized AE with L2 decoder param (RAE_L2),Regularized AE with gradient penalty (RAE_GP),Riemannian Hamiltonian VAE (RHVAE) https://github.com/zc8340311/RobustAutoencoder Applications of AutoEncoders,Dimensionality reduction,Anomaly detection,Image denoising,Image compression,Image generation 6.BoltzmannMachines,Restricted Boltzmann Machine,deep belief network,deep BoltzmannMachines 7.Self Organizing Maps (SOM) , Fast Self-Organizing Map https://github.com/nmarincic/numbasom,minisom https://github.com/JustGlowing/minisom 8.Natural language processing regex,PRegEx (https://github.com/manoss96/pregex) Clean data(removing stopwords depond on problem ,lowering data,tokenization,postagging,stemmimg or lemmatization depond on problem,skipgram,n-gram,chunking) clean text https://github.com/jfilter/clean-text Cleaning and Pre-processing textual data with NeatText library Automated NLP Pre-Processing using Data-Purifier Library https://github.com/Elysian01/Data-Purifier Nltk,spacy,genism,textblob,inltk,Indic NLP,StanfordNLP,Pattern,stanza,OpenNLP,polygot,corenlp,polyglot,PyDictionary,Huggiing face,spark nlp,allen nlp,rasa nlu,Megatron,texthero,Flair,textacy,finetune,gluon-nlp,VnCoreNLP,fasttext,Langid,PyCLD3,Guesslang,Parrot libraries keyword library Rake_NLTK, Spacy, Textrank, Word cloud, KeyBert, Yake, MonkeyLearn API and Textrazor API. jiant is an NLP toolkit https://github.com/nyu-mll/jiant clean-text https://github.com/jfilter/clean-text https://www.youtube.com/watch?v=i2TjAgga1YU indicnlp https://indicnlp.ai4bharat.org/samanantar/ Augmenting Data For NLP Tasks https://towardsdatascience.com/tips-tricks-augmenting-data-for-nlp-tasks-983e33ad55a7 https://amitness.com/2020/05/data-augmentation-for-nlp/ https://github.com/makcedward/nlpaug https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28 NLP Data Augmenting https://lnkd.in/eHa2cH6 Text Data Augmentation in Natural Language Processing with Texattack https://www.analyticsvidhya.com/blog/2022/02/text-data-augmentation-in-natural-language-processing-with-texattack/ Tagalog is our state-of-the-art solution for data management and labeling in Natural Language Processing https://www.tagalog.ai/tagalog/ https://github.com/jasonwei20/eda_nlp https://github.com/dsfsi/textaugment https://github.com/QData/TextAttack https://github.com/makcedward/nlpaug nlp_profiler https://analyticsindiamag.com/complete-guide-on-nlp-profiler-python-tool-for-profiling-of-textual-dataset/ doccano text annotation tool https://github.com/doccano/doccano https://www.youtube.com/watch?v=vT-GE_jssPk https://github.com/doccano/auto-labeling-pipeline https://github.com/doccano/doccano-client https://doccano.herokuapp.com/ Data augmentation for NLP-https://github.com/makcedward/nlpaug Data Augmentation library for text nlpaug https://towardsdatascience.com/data-augmentation-library-for-text-9661736b13ff doccano,Parrot_Paraphraser,NLPAug,AugLy detext-https://github.com/linkedin/detext nlpaug-https://github.com/makcedward/nlpaug augmenty https://github.com/KennethEnevoldsen/augmenty NLP-progress -https://github.com/sebastianruder/NLP-progress Super Duper NLP Repo- https://notebooks.quantumstat.com/ Multilingual Representations for Indian Languages https://tfhub.dev/google/MuRIL/1 Natural Language Processing 365- https://ryanong.co.uk/natural-language-processing-365/ 1 line for hundreds of NLP models and algorithms- https://github.com/JohnSnowLabs/nlu simpletransformers beautiful Wordclouds in Python https://towardsdatascience.com/how-to-easily-make-beautiful-wordclouds-in-python-55789102f6f5 Automate your Text Processing workflow in a single line of Python Code https://towardsdatascience.com/automate-your-text-processing-workflow-in-a-single-line-of-python-code-e276755e45de quantumstat https://index.quantumstat.com/ Dynaboard: Moving beyond accuracy to holistic model evaluation in NLP https://ai.facebook.com/blog/dynaboard-moving-beyond-accuracy-to-holistic-model-evaluation-in-nlp/ gobbli for interactive NLP https://medium.com/rti-cds/using-gobbli-for-interactive-nlp-f60feb41e5cb AutoReg Regex of string in Python https://github.com/SusmitPanda/AutoReg Negation Handling Increasing Accuracy of Sentiment Classification NLU,NLG,NER,text summarization,Sentiment Analysis,Text Classifications,machine translation,chat bot,Text Generation,Speech Recognition Case Normalization,regex,Lowercasing,sent_tokenize,Tokenization,Remove Punctuations,Removing Stopwords,Removing Unicode,Removal of(Noise, URLs, Hashtag and User-mentions Hashtag),Replacing Emoticons,Removing Number,Correction of Spelling mistakes,Expanding Contractions,Removing Emojis,Convert Emoji,Remove Emoticon,Removing URLs,Hashtags,text normalization,Noise Removal,Punctuation,Spell Correction,Stemming or Lemmatization 1.One-hot-encoding,Index-based Encoding,Term Frequency,bag of words ,Bag of N-grams Model,Binary Term Frequency,(L1) Normalized Term Frequency,(L2) Normalized TF-IDF 2.Tfidf ,Weighted Class TF-IDF,tfidf + CHI²,HashingVectorizer 3.wordembedding : Use a pre-trained model , Self-Trained model a.using pretrained model i)word2vec( cbow,skipgram) ,AvgWord2vec ii)glove https://medium.com/spark-nlp/1-line-to-glove-word-embeddings-with-nlu-in-python-baed152fff4d iii)fast text iv)MetaVec b.creating own embedding (use when have huge data) i)word2vec library ii)keras embedding elmo (store semantic of word) Context-independent Context-independent without machine learning Bag-of-words,TF-IDF Context-independent with machine learning Word2vec (Bag of Words (CBoW) and Skip-Gram ) GloVe fastText Context-dependent Context-dependent and RNN based(elmo,cove) Context-dependent and transformer-based (BERT ,xlm,RoBERTa,ALBERT) contextual embeddings: AllenNLP ELMo, OpenAI’s GPT,GPT1,GPT2,GPT3, and Google’s BERT Fast_Sentence_Embeddings Compute Sentence Embeddings Fast https://github.com/oborchers/Fast_Sentence_Embeddings Universal Embeddings, Contextual Embeddings (Transformers),BERT Embeddings,Sentence Transformers,Sentence Vectors,Sentence Embedding Transformer based embedding 3 b Tokenizer nlp(texs_to_sequences ) 4.Document embedding-Doc2vec 5.sentence embedding sense2vec,SENT2VEC,Universal sentence encoder,Sentence Transformers Top2Vec Topic Modelling https://towardsdatascience.com/april-edition-adventures-in-topic-modelling-7ee9081a48a0 Doc2Vec Distributed memory model , Distributed bag of word,Node2Vec,Top2Vec,Doc2Vec,Item2Vec Elmo, BERT,Universal Sentence Encoder, Sentence Transformers 6.using rnn,lstm,gru Conventional RNN,Deep Transition RNN,DT(S)-RNN,DOT-RNN,Stacked RNN for above 3 models have bidirectional also textgenrnn generate text https://github.com/minimaxir/textgenrnn 7.Encoder and Decoder(sequence to sequence), ProphetNet(new pretrained seq2seq model) 8.attention self attention,Global Attention,Multi-Head Attention,Local Attention (monotonic,predictive),flash-attention,Fast and memory-efficient exact attention https://github.com/uzaymacar/attention-mechanisms Seq2seq with Attention,Self-attentionm,Multi-head Attention 9.Transformer (big breakthrough in NLP) - http://jalammar.github.io/illustrated-transformer/ Build a Transformer in JAX from scratch https://theaisummer.com/jax-transformer/ Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing https://github.com/nlp-uoregon/trankit FastFormers https://medium.com/ai-in-plain-english/fastformers-233x-faster-transformers-inference-on-cpu-4c0b7a720e1 Shrinking Transformers (reduce size) 1.quantization,distillation,pruning, Reformer,Performers,vision transformer Reformer: The Efficient Transformer Longformer: The Long-Document Transformer ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators DeLighT: Deep and Light-weight Transformer https://analyticsindiamag.com/complete-guide-to-delight-deep-and-light-weight-transformer/ https://github.com/balavenkatesh3322/NLP-pretrained-model Tree-Transformer https://github.com/yaushian/Tree-Transformer Scalable Transformer-based Model https://analyticsindiamag.com/guide-to-perceiver-a-scalable-transformer-based-model/ Transformers Interpret https://towardsdatascience.com/introducing-transformers-interpret-explainable-ai-for-transformers-890a403a9470 https://github.com/cdpierse/transformers-interpret https://analyticsindiamag.com/hands-on-guide-to-the-evolved-transformer-on-neural-machine-translation/ Novel Interpretable Transformer https://github.com/hila-chefer/Transformer-Explainability https://analyticsindiamag.com/compute-relevancy-of-transformer-networks-via-novel-interpretable-transformer/ https://www.kdnuggets.com/2021/02/hugging-face-transformer-basics.html#.YE7gRy9s-LA.linkedin mBART-50 https://www.youtube.com/watch?v=fxZtz0LPJLE&feature=youtu.be Few-shot classification with SetFit and a custom dataset https://rubrix.readthedocs.io/en/docs-setfit_tutorial/tutorials/few-shot-classification-with-setfit.html 10.BERT,Packed BERT,BART,DynaBERT,SBERT,ConvBert,Quantized MobileBERT,ALBERT,ELECTRA,ARBERT,MARBERTElectra,Transformer-XL,Longformer,Reformer,DistilBERT,ELMo,ROBERTA,XLNet,XLM-RoBERTa,DeBERTa,T5,fastT5, CodeT5,mT5,ByT5,simpleT5,byt5,OnnxT5,FastT5,Linformer,DISTILBERT,GPT,GPT2,GPT3,gpt-neo,gpt-neox,GPT-J,aitextgen,PRADO,PET,BORT,MuRIL,Multitask Unified Model,aitextgen,AI21's 'Jurassic' language model,Turing NLG,Wu Dao 2.0,PanGu-Alpha,Gopher,Megatron model https://neptune.ai/blog/bert-and-the-transformer-architecture-reshaping-the-ai-landscape gpt3 https://www.producthunt.com/posts/100-resources-on-gpt-3 Graph4NLP https://dlg4nlp.github.io/index.html Feedback Transformers from Facebook AI https://towardsdatascience.com/feedback-transformers-from-facebook-ai-221c5dd09e3f DETR https://analyticsindiamag.com/how-to-detect-objects-with-detection-transformers/ https://github.com/dddzg/up-detr DeiT https://analyticsindiamag.com/introducing-deit-data-efficient-image-transformers/ https://github.com/facebookresearch/deit 80+ NLP tasks https://medium.com/innerdoc/80-natural-language-processing-tasks-described-c777bc4974b3 Text-to-Image https://www.datasciencecentral.com/profiles/blogs/summarizing-popular-text-to-image-synthesis-methods-with-python NLP: Pre-trained Sentiment Analysis https://medium.com/@b.terryjack/nlp-pre-trained-sentiment-analysis-1eb52a9d742c Awesome-NLP-Resources -https://github.com/Robofied/Awesome-NLP-Resources https://shivanandroy.com/awesome-nlp-resources/ https://github.com/keon/awesome-nlp 10 Popular Keyword Extraction Algorithms in Natural Language Processing https://prakhar-mishra.medium.com/10-popular-keyword-extraction-algorithms-in-natural-language-processing-8975ada5750c https://medium.com/@jatinmandav3/opinion-mining-sometimes-known-as-sentiment-analysis-or-emotion-ai-refers-to-the-use-of-natural-874f369194c0#:~:text=fastText%20is%20a%20library%20for,pretrained%20models%20for%20294%20languages https://analyticsindiamag.com/top-ten-bert-alternatives-for-nlu-projects/ https://towardsdatascience.com/from-pre-trained-word-embeddings-to-pre-trained-language-models-focus-on-bert-343815627598 GPT2 generated Indian Food Recipes https://www.kaggle.com/nulldata/gpt2-generated-indian-food-recipes http://jalammar.github.io/ http://jalammar.github.io/illustrated-bert/ http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/ https://jalammar.github.io/explaining-transformers/ https://jalammar.github.io/hidden-states/ https://www.kdnuggets.com/2019/09/bert-roberta-distilbert-xlnet-one-use.html 11.Speech (Braina,Dragon Speech Recognition Solutions ,Winscribe,Gboard,Windows 10 Speech Recognition,Otter,Speechnotes,tts,OpenSpeech,FRILL,Vakyansh) audio data augmentation https://github.com/iver56/audiomentations speech to text text to speech https://towardsdatascience.com/text-to-speech-one-small-step-by-mankind-to-create-lifelike-robots-54e19f843b21 Acoustic model,Speaker diarisation,apis,apiai,assemblyai,google-cloud-speech,pocketsphinx,SpeechRecognition,watson-developer-cloud,wit,Coqui TTS,Mozilla TTS, OpenTTS,ESPNet,PaddleSpeech,Wav2Vec, Whisper, DeepSpeech,Eesen,TensorFlowASR,Vosk,CMUSphinx,Pocketsphinx,KoNLPy,Madmom,HTK,Pysptk,Tortoise TTS,Bark,Musicgen,Riffusion Microsoft IceCAPS is an Open Source Framework for Conversational Modeling https://pub.towardsai.net/microsoft-icecaps-is-an-open-source-framework-for-conversational-modeling-4f78492ca685 State-of-the-art Approaches to Building Open-Domain Conversational Agents https://www.topbots.com/conversational-ai-open-domain-chatbots/?utm_source=twitter&utm_medium=company_post&utm_campaign=conversational_open_domain_chatbots LaMDA: our breakthrough conversation technology https://www.blog.google/technology/ai/lamda assemblyai https://www.assemblyai.com/ bark https://github.com/suno-ai/bark SpeechBrain A PyTorch Powered Speech Toolkit https://speechbrain.github.io/ https://github.com/speechbrain/speechbrain Wav2vec-U learns to recognize #speech from unlabeled data https://venturebeat.com/2021/05/21/facebook-wav2vec-u-learns-to-recognize-speech-from-unlabeled-data/?utm_source=dlvr.it&utm_medium=linkedin Wav2Vec2 https://huggingface.co/transformers/model_doc/wav2vec2.html https://www.youtube.com/watch?v=dJAoK5zK36M&feature=youtu.be SincNet is a neural architecture for efficiently processing raw audio samples https://github.com/mravanelli/SincNet HuggingFace Transformers ASR https://github.com/dennisbakhuis/Ecare_Brunch_ASR English speech recognition https://github.com/openai/whisper https://github.com/balavenkatesh3322/audio-pretrained-model SpeechRecognition ASR2K: Speech Recognition https://github.com/xinjli/asr2k audiomentations Python library for audio data augmentation https://github.com/iver56/audiomentations googletrans (google Translator) https://pypi.org/project/googletrans/ lang-identification Google Compact Language Detector,FastText 𝗴𝗧𝗧𝗦 for text to speech conversion , 𝘀𝗽𝗲𝗲𝗰𝗵_𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻,TTS Python/Pytorch app for easily synthesising human voices https://github.com/BenAAndrew/Voice-Cloning-App Speech-Transformer-tf2.0 https://github.com/xingchensong/Speech-Transformer-tf2.0 The Super Duper NLP Repo https://notebooks.quantumstat.com/ ecco https://github.com/jalammar/ecco https://www.eccox.io/ https://www.youtube.com/watch?v=rHrItfNeuh0&feature=youtu.be Language Interpretability Tool (LIT) is an open-source platform for visualization and understanding of NLP models https://pair-code.github.io/lit/ Language Interpretability Tool https://github.com/pair-code/lit https://ai.googleblog.com/2020/11/the-language-interpretability-tool-lit.html autonlp https://analyticsindiamag.com/hands-on-guide-to-using-autonlp-for-automating-sentiment-analysis/ https://medium.com/towards-artificial-intelligence/natural-language-processing-nlp-with-python-tutorial-for-beginners-1f54e610a1a0 https://pakodas.substack.com/p/neural-search-on-indian-languages https://www.linkedin.com/pulse/natural-language-processing-2020-year-review-ivan-bilan/?trackingId=CYfd1ZyLStu6x09tjVIoGw%3D%3D ConvBert https://github.com/yitu-opensource/ConvBert Python interface for building, loading, and using GloVe vectors https://github.com/Lguyogiro/pyglove SentenceTransformers https://www.sbert.net/ Reformer – The Efficient Transformer https://analyticsindiamag.com/hands-on-guide-to-reformer-the-efficient-transformer/ Funnel-Transformer https://github.com/laiguokun/Funnel-Transformer CLIP – Connecting Text To Images https://analyticsindiamag.com/hands-on-guide-to-openais-clip-connecting-text-to-images/ Topic Modeling in One Line with Top2Vec https://towardsdatascience.com/topic-modeling-in-one-line-with-top2vec-a413991aa0ef MT5-https://venturebeat.com/2020/10/26/google-open-sources-mt5-a-multilingual-model-trained-on-over-101-languages/?utm_content=144321587&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012 VADER does not require any training data https://pypi.org/project/vaderSentiment/ https://analyticsindiamag.com/sentiment-analysis-made-easy-using-vader/ APPLICATIONS OF MACHINE TRANSLATIO-Text-to-text,Text-to-speech,Speech-to-text,Speech-to-speech,Image (of words)-to-text Google-GNMT (Tensorflow),Facebook-fairseq (Torch),Amazon-Sockeye (MXNet),NEMATUS (Theano),THUMT (Theano),OpenNMT (PyTorch),StanfordNMT (Matlab),DyNet-lamtram(CMU),EUREKA(MangoNMT awesome-gpt3 https://github.com/elyase/awesome-gpt3 Robustness Gym: Evaluation Toolkit for NLP https://github.com/robustness-gym/robustness-gym https://analyticsindiamag.com/best-nlp-based-seo-tools-for-2021/ https://towardsdatascience.com/5-nlp-models-that-you-need-to-know-about-754594a3225b https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html https://analyticsindiamag.com/flair-hands-on-guide-to-robust-nlp-framework-built-upon-pytorch/ https://medium.com/modern-nlp/nlp-metablog-a-blog-of-blogs-693e3a8f1e0c summarization https://github.com/hyunwoongko/summarizers ctrl-sum https://github.com/salesforce/ctrl-sum classification,clustering,recommender systems,topic modelling,sentiment analysis,semantic analysis,summarization,machine translation,conversational interface,named entity recognition F.Time Series Hands-On Guide To Atspy For Automating The Time-Series Forecasting https://github.com/Apress/hands-on-time-series-analylsis-python here data split is different (train,test,validate) here handling missing data different Time Series Decomposition In Python trend, seasonality,Cyclical and noise https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2 Removing trend Differencing,Least square trends removal Converting Non- stationary into stationary Detrending,Differencing,Transformation Time Series Decomposition log,box-cox transformation,moving average Removing seasonality Seasonal differencing,Seasonal means,Method of moving averages generally used to impute data in Time Series 1.ffill 2.bfill 3.do mean of previous or future x samples and impute 4.take previous season value and impute (data with trend) 5.mean,mode,median,random sample imputation (data without trend and without seasonality) 6.linear interpolation(data with trend and without seasonality) 7.seasonal +interpolation(data with trend and with seasonality) here model selection deponds on different property of data like stationary,trend,seasonality,cyclic Anomaly Detection using Isolation Forest,AutoEncoders Granger Causality Statistical Test use for variable usable for forecast adfuller test for Stationarity Non Stationary Statistical Test - KPSS and ADF ACF, PACF, decomposition, ADF test Handling Data with Regular Gaps using Facebook Prophet models 1.AR,VR, VAR, MA, ARMA, ARIMA, auto arima(pmd arima) ,seasonal arima(SARIMA),SARIMAX models 2.Autoregressive,Vector Autoregression,Vector Autoregression Moving-Average,Vector Autoregression Moving-Average with Exogenous Regressors 3.Moving average,Exponential Moving average,Exponential Smoothing,Simple average, Holt’s linear trend method, Holt’s Winter seasonal method,DeepAR,N-BEATS 11 Classical Time Series Forecasting Methods in Python https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/ 4.XGBoost,Lstm(neural network),DeepAR ( An RNN Algorithm) 5.GARCH atspy Automated time-series models 6.Navie forecasts 7.Smoothing (moving average,exponential smoothing) 8.Facebook prophet (note:expceted date column as ds and target column as y) https://thecleverprogrammer.com/2020/12/14/facebook-prophet-model-with-python/ NeuralProphet Model- https://ourownstory.github.io/neural_prophet/model-overview/ https://thecleverprogrammer.com/2021/01/28/neuralprophet-model-with-python/ bulbea Deep Learning based Python Library for Stock Market Prediction and Modelling https://github.com/achillesrasquinha/bulbea PyTorch Forecasting enables deep learning models for time-series forecasting pytorch-ts https://github.com/zalandoresearch/pytorch-ts ETSformer-pytorch https://github.com/lucidrains/ETSformer-pytorch Transformer Networks to build a Forecasting model https://towardsdatascience.com/how-to-use-transformer-networks-to-build-a-forecasting-model-297f9270e630 Temporal Fusion Transformer (By Google) hmmlearn https://github.com/ushareng/StockPricePredictionUsingHMM_Byte/blob/master/StockPricePredictionUsingHMM.ipynb pyramid-arima https://github.com/tgsmith61591/pyramid pyflux: time series library: https://github.com/RJT1990/pyflux orbit https://eng.uber.com/orbit/ greykite A flexible, intuitive and fast forecasting library https://github.com/linkedin/greykite https://www.analyticsvidhya.com/blog/2021/05/greykite-time-series-forecasting-in-python/ Silverkite LinkedIn open-sources Greykite, a library for time series forecasting https://github.com/linkedin/greykite/stargazers stumpy https://github.com/TDAmeritrade/stumpy Giotto-Time Time-Series Forecasting Python Library https://github.com/giotto-ai/giotto-time https://analyticsindiamag.com/guide-to-giotto-time-a-time-series-forecasting-python-library/ Informer (for Long Sequence Time-Series Forecasting) https://analyticsindiamag.com/informer/ tfcausalimpact https://github.com/WillianFuks/tfcausalimpact deepar is global model https://www.youtube.com/watch?v=xcbj0RE3kfI&list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK&index=14 pmdarima for Auto ARIMA GluonTS https://github.com/awslabs/gluon-ts sktime — a unified time-series framework for Scikit-Learn tsfresh — a magical library for feature extraction in time-series datasets ThymeBoost Forecasting with Gradient Boosted Time Series Decomposition https://github.com/tblume1992/ThymeBoost darts A python library for easy manipulation and forecasting of time series https://github.com/unit8co/darts Kats https://github.com/facebookresearch/Kats Time Series Outlier Detection with ThymeBoost AtsPy: Automated Time Series Models in Python https://github.com/firmai/atspy Merlion: A Machine Learning Framework for Time Series Intelligence https://github.com/salesforce/Merlion stumpy powerful and scalable Python library for modern time series analysis https://github.com/TDAmeritrade/stumpy mlforecast Scalable machine learning based time series forecasting https://github.com/Nixtla/mlforecast statsforecast Lightning ⚡️ fast forecasting with statistical and econometric models https://github.com/Nixtla/statsforecast 9.Holts winter,Holts linear trend 10.Auto_Timeseries by auto-ts https://www.youtube.com/watch?v=URUiVD37fns&list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK&index=24 tell best model for data AutoTS-https://analyticsindiamag.com/hands-on-guide-to-autots-effective-model-selection-for-multiple-time-series/ https://github.com/AutoViML/Auto_TS Automated Time Series Forecasting https://github.com/winedarksea/AutoTS , No-Code AI Forecasting Platform https://datafloat.ai/ AutoML for time series: advanced approaches with FEDOT framework https://towardsdatascience.com/automl-for-time-series-advanced-approaches-with-fedot-framework-4f9d8ea3382c AutoML for time series: definitely a good idea https://towardsdatascience.com/automl-for-time-series-definitely-a-good-idea-c51d39b2b3f AutoReg Regex of string in Python https://github.com/SusmitPanda/AutoReg pytsal low-code open-source python framework for Time Series analysis,visualization,forecasting along with AutoTS https://github.com/KrishnanSG/pytsal Automated Time Series Forecasting https://github.com/winedarksea/AutoTS Forecasting with H2O AutoML https://github.com/business-science/modeltime.h2o/ Forecasting Stock Prices Using Stocker https://medium.com/mlearning-ai/forecasting-stock-prices-using-stocker-7d2ac15966f5 MiniRocket: Fast(er) and Accurate Time Series Classification https://towardsdatascience.com/minirocket-fast-er-and-accurate-time-series-classification-cdacca2dcbfa modeltime https://github.com/business-science/modeltime GluonTS , PytorchTS https://analyticsindiamag.com/gluonts-pytorchts-for-time-series-forecasting/ stocker https://medium.datadriveninvestor.com/forecasting-stock-prices-using-stocker-66503c26307a 11.Temporal Convolutional Neural 12.Atspy For Automating The Time-Series Forecasting-https://analyticsindiamag.com/hands-on-guide-to-atspy-for-automating-the-time-series-forecasting/ 13.Darts-https://analyticsindiamag.com/hands-on-guide-to-darts-a-python-tool-for-time-series-forecasting/ 14.Bayesian Neural Network , TsEuler 15.PyFlux (easy way to compare different models)-https://analyticsindiamag.com/pyflux-guide-python-library-for-time-series-analysis-and-prediction/ 16.Orbit , DeepAR ,NeuralProphet(https://github.com/ourownstory/neural_prophet https://ourownstory.github.io/neural_prophet/model-overview/) IBM’s AutoAI automates time series forecasting https://www.ibm.com/blogs/research/2021/03/autoai-time-series/?utm_campaign=Learning%20Posts&utm_content=159454790&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323 Kats all in 1 time seres data https://github.com/facebookresearch/kats https://facebookresearch.github.io/Kats/ orbit https://analyticsindiamag.com/hands-on-guide-to-orbit-ubers-python-framework-for-bayesian-forecasting-inference/ https://github.com/uber/orbit best article-https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/ TimeSynth https://github.com/TimeSynth/TimeSynth https://analyticsindiamag.com/guide-to-timesynth-a-python-library-for-synthetic-time-series-generation/ time series visualization tool https://plotjuggler.io/ Time Series Anomaly Detection using Generative Adversarial Networks(TadGAN) https://analyticsindiamag.com/hands-on-guide-to-tadgan-with-python-codes/ fastquant — Backtest and optimize your trading strategies with only 3 lines of code https://github.com/enzoampil/fastquant pytorch-forecasting https://github.com/jdb78/pytorch-forecasting https://analyticsindiamag.com/guide-to-pytorch-time-series-forecasting/ https://pytorch-forecasting.readthedocs.io/en/latest/ https://pytorch-forecasting.readthedocs.io/en/latest/tutorials/ar.html Complex Exponential Smoothing (CES) which can handle both stationary and non-stationary processes and model a wide spectum of level and trend time-series. https://github.com/Nixtla/statsforecast/tree/main/experiments/ces sktime-https://github.com/alan-turing-institute/sktime https://analyticsindiamag.com/sktime-library/ autocast https://github.com/andyzoujm/autocast tsfresh – a magical library for feature extraction in time-series datasets. atspy https://github.com/firmai/atspy tcn https://towardsdatascience.com/farewell-rnns-welcome-tcns-dd76674707c8 Pastas https://analyticsindiamag.com/guide-to-pastas-a-python-framework-for-hydrogeological-time-series-analysis/ https://github.com/pastas/pastas stockDL https://github.com/ashishpapanai/stockDL decompsition https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2 Bayesian Diffusion Modeling https://www.topbots.com/bayesian-diffusion-modeling/ Top 10 Python Tools For Time Series Analysis https://analyticsindiamag.com/top-10-python-tools-for-time-series-analysis/ fine Tune Your Machine Learning Models To Improve Forecasting Accuracy https://www.kdnuggets.com/2019/01/fine-tune-machine-learning-models-forecasting.html add extra features https://towardsdatascience.com/the-demand-sales-forecast-technique-every-data-scientist-should-be-using-to-reduce-error-1c6f25add9cb https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/ https://www.machinelearningplus.com/time-series/time-series-analysis-python/ https://www.datasciencecentral.com/profiles/blogs/list-of-time-series-methods-in-one-picture https://github.com/Apress/hands-on-time-series-analylsis-python https://otexts.com/fpp2/simple-methods.html https://analyticsindiamag.com/top-time-series-deep-learning-methods/ book https://otexts.com/fpp2/ deep_autoviml Build tensorflow keras model pipelines in a single line of code https://github.com/AutoViML/deep_autoviml G.𝐆𝐫𝐚𝐩𝐡 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 Spatial-temporal graph neural networks,Structural Deep Network Embedding,Convolutional Graph Neural Network,GraphSAGE,Graph convolutional recurrent network,Diffusion convolutional recurrent neural network,Graph LSTM,Graph Autoencoders,Variational Graph Auto-Encoders,Graph Attention Networks G.Semi supervised learning,Self-Supervised Learning,Multi-Instance Learning self-training meta-estimator for semi-supervised learning skweak: A Python Toolkit For Applying Weak Supervision To NLP Tasks https://analyticsindiamag.com/meet-skweak-a-python-toolkit-for-applying-weak-supervision-to-nlp-tasks/ 10 Self-Supervised Learning Frameworks & Libraries To Use In 2021 analyticsindiamag.com/10-self-supervised-learning-frameworks-libraries-to-use-in-2021/ Self-Supervised Learning https://github.com/jason718/awesome-self-supervised-learning OpenMMLab Self-Supervised Learning https://github.com/open-mmlab/mmselfsup awesome-self-supervised-learning https://github.com/jason718/awesome-self-supervised-learning Self-supervised Video Object Segmentation https://charigyang.github.io/motiongroup/ lightly A python library for self-supervised learning on images https://github.com/lightly-ai/lightly Weak Supervision: The Art Of Training ML Models From Noisy Data https://analyticsindiamag.com/weak-supervision-the-art-of-training-ml-models-from-noisy-data/ snorkel and skweak, are there other libraries to explore for weak supervision in NLP 8 Resources To Learn Self-Supervised Learning In 2021 https://analyticsindiamag.com/top-8-resources-to-learn-self-supervised-learning-in-2021/ Barlow Twins: Self-Supervised Learning via Redundancy Reduction https://analyticsindiamag.com/a-guide-to-barlow-twins-self-supervised-learning-via-redundancy-reduction/ https://github.com/facebookresearch/barlowtwins skweak: A Python Toolkit For Applying Weak Supervision To NLP Tasks https://analyticsindiamag.com/meet-skweak-a-python-toolkit-for-applying-weak-supervision-to-nlp-tasks/ H.Active learning,Multi-Task Learning,Online Learning Active Learning Frameworks https://towardsdatascience.com/a-summary-of-active-learning-frameworks-3165159baae9 Meta Learning https://github.com/sudharsan13296/Awesome-Meta-Learning Avalanche: A Python Library for Continual Learning https://analyticsindiamag.com/avalanche-a-python-library-for-continual-learning/ Reptile (OpenAI’s Latest Meta-Learning Algorithm) https://github.com/openai/supervised-reptile https://analyticsindiamag.com/reptile-openais-latest-meta-learning-algorithm/ Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" https://github.com/cbfinn/maml I.Transfer learning(Inductive Transfer learning(similar domain,different task),Unsupervised Transfer Learning(different task,different domain but similar enough) ,Transductive Transfer Learning(similar task,different domain)),Inductive transfer learning(labeled data is the same for the target and source domain but the tasks the model works on are different),Unsupervised transfer learning(unsupervised tasks for both source and target tasks),self taught learning,Homogeneous Transfer Learning,Heterogenous Transfer Learning Transfer Learning Using TensorFlow Keras https://analyticsindiamag.com/transfer-learning-using-tensorflow-keras/ https://github.com/artix41/awesome-transfer-learning https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a J.Deep dream,Style transfer K.One-shot learning,Zero-shot learning l.Incremental Training https://blog.rasa.com/rasa-new-incremental-training/ https://github.com/ChristosChristofidis/awesome-deep-learning 101 Machine Learning Algorithms for Data Science with Cheat Sheets https://blog-datasciencedojo-com.cdn.ampproject.org/c/s/blog.datasciencedojo.com/machine-learning-algorithms/amp/ TYPES OF ACTIVATION FUNCTIONS: LINEAR ACTIVATION,RELU,LEAKY RELU,GELU,Parameterized ReLU,Shifted ReLU, Noisy ReLU,SIGMOID ACTIVATION,TANH ACTIVATION,elu,PReLU,Modifying ReLU,Shifted ReLU,Softmax,Swish,Softplus,Mish,Smooth reLU,GELU,Swish,Elliot Optimizer- Gradient Descent(Batch Gradient Descent,Stochastic Gradient Descent,Mini batch Gradient Descent),sgd with momentum,Adagrad,RMSProp,AMSGrad,Adam,AdaBelief,MADGRAD,Nero, https://analyticsindiamag.com/ultimate-guide-to-pytorch-optimizers/ https://analyticsindiamag.com/guide-to-tensorflow-keras-optimizers/ Regularization- L1, L2,elasticnet, dropout, early stopping, and data augmentation,batch normalisation,Layer Normalization,Group Normalization,tree purning,DropBlock,DropConnect,Learning rate schedulingWeight Decay,Gradient clipping,Adaptive optimizer Addressing Overfitting - 13 Methods 01. Dimensionality Reduction 02. Feature Selection 03. Early Stopping 04. K-Fold Cross-Validation 05. Creating Ensembles 06. Pre‐Pruning 07. Post‐Pruning 08. Noise Regularization 09. Dropout Regularization 10. L1 and L2 Regularization 11. Data (Image) Augmentation 12. Adding More Training Data 13. Reducing Network Width & Depth DropBlock: A New Regularization Technique https://pub.towardsai.net/dropblock-a-new-regularization-technique-e926bbc74adb Learning rate scheduling (Learning rate finder),Weight Decay,Gradient clipping,Cyclic Learning Rate weight initialization Normal Distribution,initialized to the same value,Xavier Initialization,He Norm Initialization, Different Normalization Layers - https://towardsdatascience.com/different-normalization-layers-in-deep-learning-1a7214ff71d6 Hyperparameters Number of hidden layers,Dropout,activation function,Weights initialization , learning rate,epoch, iterations and batch size DropBlock-Keras-Implementation https://github.com/iantimmis/DropBlock-Keras-Implementation https://github.com/miguelvr/dropblock https://github.com/DHZS/tf-dropblock standard dropout,early dropout,late dropout ***Hyperparameter tuning*** https://analyticsindiamag.com/top-8-approaches-for-tuning-hyperparameters-of-machine-learning-models/ https://analyticsindiamag.com/top-10-open-source-hyperparameter-optimisation-libraries-for-ml-models/ https://github.com/balavenkatesh3322/hyperparameter_tuning A.manual search a.GridSearchCV (check every given parameter so take long time),TuneGridSearchCV HalvingGridSearch https://towardsdatascience.com/11-times-faster-hyperparameter-tuning-with-halvinggridsearch-232ed0160155 https://towardsdatascience.com/faster-hyperparameter-tuning-with-scikit-learn-71aa76d06f12 tune-sklearn https://github.com/ray-project/tune-sklearn (TuneGridSearchCV) b.RandomizedSearchCV (search randomly narrow down our time) with Scikit-learn, Scikit-Optimize,Hyperopt,TuneSearchCV HalvingRandomSearchCV c.Optuna,Hyperopt,Scikit-optimize,Keras Tuner,Ray-tune,Bayesian Optimization,Bayesian Optimization with Gaussian Processes (BO-GP),Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE),Particle swarm optimization (PSO),Genetic algorithm (GA)Hyperopt,bayes search,Hyperband and BOHB,HyperOpt-Sklearn,Bayes Search,Scikit Optimize,TPE,Multivariate TPE,HyperBand,Bayesian Optimization,exhaustive search, heuristic search,multi-fidelity optimization,NNI,DEAP,OptFormer,hgboost,Hyperopt,Sklearn-genetic,GPyOpt,pyGPGO,Mango,mlmachine,Polyaxon,BayesianOptimization,Talos,SHERPA,Scikit-Optimize,GPyOpt,SMAC, Simulated annealing (SA),Genetic algorithms (GAs),Particle swarm optimization (PSO),Population-Based Training (PBT),Multi-Fidelity Optimization,DEAP,SMAC,Ray Tune,Google’s Vizer, Microsoft’s NNI,Keras tuner,BayesianOptimization,GPyOpt,SigOpt Bayesian Optimization: https://github.com/fmfn/BayesianOptimization Scikit Optimize: https://github.com/scikit-optimize/scikit-optimize Pyro: https://github.com/pyro-ppl/pyro BoTorch: https://github.com/pytorch/botorch RBFOpt library for black-box optimization https://github.com/coin-or/rbfopt Bayesian search with Gaussian processes,bayesian search with Random Forests,Bayesian search with GBMs Bayesian Optimization Using BoTorch https://analyticsindiamag.com/guide-to-bayesian-optimization-using-botorch/ hyperparameter optimization https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms Hyperopt hyperas https://www.kdnuggets.com/2018/12/keras-hyperparameter-tuning-google-colab-hyperas.html hyperopt http://hyperopt.github.io/hyperopt/ hypertune-using-scikit-optimize BayesSearchCV HpBandSter https://github.com/automl/HpBandSter hpsklearn https://medium.com/mlearning-ai/automatic-hyperparameter-optimization-6a1692c2ebee hypopt https://github.com/cgnorthcutt/hypopt https://medium.com/mlearning-ai/automatic-hyperparameter-optimization-6a1692c2ebee HiPlot https://analyticsindiamag.com/this-new-tool-helps-developers-in-effective-hyperparameter-tuning/ botorch Bayesian optimization https://github.com/pytorch/botorch OCTIS https://github.com/mind-lab/octis hyperband https://neptune.ai/blog/hyperband-and-bohb-understanding-state-of-the-art-hyperparameter-optimization-algorithms Spearmint https://github.com/JasperSnoek/spearmint/ tuun Hyperparameter tuning via uncertainty modeling https://github.com/petuum/tuun tune-sklearn https://github.com/ray-project/tune-sklearn/ NeuPy http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html#id24 Vizier ConfigSpace https://automl.github.io/ConfigSpace/master/ https://towardsdatascience.com/tuning-xgboost-with-xgboost-writing-your-own-hyper-parameters-optimization-engine-a593498b5fba NatureInspiredSearchCV https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms d.Sequential Model Based Optimization(Tuning a scikit-learn estimator with skopt) e.Optuna https://analyticsindiamag.com/hands-on-python-guide-to-optuna-a-new-hyperparameter-optimization-tool/ f.Genetic Algorithms,Gradient-based optimization darwin-mendel Genetic Algorithm for Hyper-Parameter Tuning https://manishagrawal-datascience.medium.com/genetic-algorithm-for-hyper-parameter-tuning-1ca29b201c08 g.Keras tuner (Random Search Keras Tuner,HyperBand Keras Tuner,Bayesian Optimization Keras Tuner,Hyperas ) https://sukanyabag.medium.com/automated-hyperparameter-tuning-with-keras-tuner-and-tensorflow-2-0-31ec83f08a62 Keras Hyperparameter Tuning with aisaratuners Library https://aisaradeepwadi.medium.com/advance-keras-hyperparameter-tuning-with-aisaratuners-library-78c488ab4d6a hyperas Automating Hyperparameter Tuning of Keras Model https://github.com/maxpumperla/hyperas storm-tuner https://github.com/ben-arnao/StoRM https://medium.com/geekculture/finding-best-hyper-parameters-for-deep-learning-model-4df7a17546c2 Hyperas https://towardsdatascience.com/automating-hyperparameter-tuning-of-keras-model-4fe69b8dedee hyperopt-sklearn https://github.com/hyperopt/hyperopt-sklearn Deep AutoViML https://github.com/AutoViML/deep_autoviml h.Scikit-Optimize,Optuna,Hyperopt,Multi-fidelity Optimization,Gradient-based optimization,Evolutionary optimization,Population-based,Bayes Search Scikit-Optimize library comes with BayesSearchCV implementation mle-hyperopt Lightweight Hyperparameter Optimization Tool https://github.com/mle-infrastructure/mle-hyperopt h.Hyperparameter Optimization https://github.com/awslabs/syne-tune i.ray[tune] and aisaratuners https://towardsdatascience.com/choosing-a-hyperparameter-tuning-library-ray-tune-or-aisaratuners-b707b175c1d7 raytune https://docs.ray.io/en/master/tune/index.html https://docs.ray.io/en/latest/tune/index.html k.model_search https://github.com/google/model_search https://analyticsindiamag.com/hands-on-guide-to-model-search-a-tensorflow-based-framework-for-automl/ Optimize machine learning models https://www.tensorflow.org/model_optimization Milano https://github.com/NVIDIA/Milano Tree-structured Parzen Estimators - TPE , TPE with Hyperopt Hyperparameter Tuning with the HParams Dashboard baytune https://www.kdnuggets.com/2021/03/automating-machine-learning-model-optimization.html Dragonfly https://analyticsindiamag.com/guide-to-scalable-and-robust-bayesian-optimization-with-dragonfly/ Pywedge https://www.analyticsvidhya.com/blog/2021/02/interactive-widget-based-hyperparameter-tuning-and-tracking-in-pywedge/ CapsNet Hyperparameter Tuning with Keras https://towardsdatascience.com/scikeras-tutorial-a-multi-input-multi-output-wrapper-for-capsnet-hyperparameter-tuning-with-keras-3127690f7f28 GPyTorch: A Python Library For Gaussian Process Models https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/ Auto-PyTorch https://github.com/automl/Auto-PyTorch l.SMAC https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/ https://towardsdatascience.com/automl-for-fast-hyperparameters-tuning-with-smac-4d70b1399ce6 m.faster Hyper Parameter Tuning(sklearn-nature-inspired-algorithms) https://pypi.org/project/sklearn-nature-inspired-algorithms/ n.talos Neural network and hyperparameter optimization using Talos https://www.analyticsvidhya.com/blog/2021/05/neural-network-and-hyperparameter-optimization-using-talos/ https://towardsdatascience.com/10-hyperparameter-optimization-frameworks-8bc87bc8b7e3 https://mlwhiz.com/blog/2020/02/22/hyperspark/?utm_campaign=100x-faster-hyperparameter-search-framework-with-pyspark&utm_medium=social_link&utm_source=missinglettr DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective https://github.com/microsoft/DeepSpeed o.shap-hypetune https://github.com/cerlymarco/shap-hypetune https://towardsdatascience.com/shap-for-feature-selection-and-hyperparameter-tuning-a330ec0ea104 mlmachine,Polyaxon,BayesianOptimization,Talos,SHERPA,Scikit-Optimize,GPyOpt p.Hyperactive https://github.com/SimonBlanke/Hyperactive Hyperopt, Optuna, and Ray,SCIKIT-OPTIMIZE,SMAC,Multi-fidelity Optimization,Successive Halving,Hyperband BOHB,SMBOSearch OMLT optimization https://github.com/cog-imperial/OMLT HyperOpt http://hyperopt.github.io/hyperopt/ Optuna https://optuna.org/ Scikit-optimize https://scikit-optimize.github.io/stable/ SigOpt https://sigopt.com/ DeepHyper Hyperparameter Search for Deep Neural Networks https://github.com/deephyper/deephyper lipo hyperparameter tuning https://github.com/jdb78/lipo Weights and Biases to Perform Hyperparameter Optimization https://hackernoon.com/using-weights-and-biases-to-perform-hyperparameter-optimization Cross validation techniques- https://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d a.Exhaustive, where the method learn and test on every single possibility of dividing the dataset into training and testing subsets. b.Non-exhaustive cross validation methods where all ways of splitting the sample are not computed. 1.Loocv 2.Kfoldcv,Repeated K-Folds Method,Shuffle & Split cross-validation 3.Stratfied cross validation,Stratified K-fold CV,Group K-fold,StratifiedGroupKFold,StratifiedShuffleSplit,Nested K-folds,Random split KFold,Walk forward,Group Time Series,Purged Group KFold,Combinatorial Purged Group KFold 4.Repeated K-folds,RepeatedStratifiedKFold,Repeated random subsampling CV 5.Holdout cross-validation 6.Repeated cross-validation,Repeated K-folds,Blocked Cross-Validation Method, Nested Cross-Validation Method,Group Cross-Validation,GroupShuffleSplit,Blocked Cross-Validation 7.LeaveOneOut,Leave P out ,Leave-one-out cross-validation,Leave-One-Group-Out Method,Leave-P-Group-Out Method 8.Time Series cross-validation,Time Series Split cross-validation ,Rolling Cross-Validation,Rolling Time Series Cross Validation,Rolling Window Cross-Validation,Monte Carlo Cross-Validation,Holdout Time Series Cross-Validation,Time Series Cross-Validation with a Gap,Sliding Time Series Cross-Validation,GapKFold,GapLeavePOut,GapRollForward 9.ShuffleSplit Cross Validation,Group Shuffle Split,Simple Time Split Validation,Sliding Window Validation,Expanding Window Validation 10.Group KFold Cross Validation 11.Monte-Carlo Cross Validation,Blocked cross-validation,Blocked K-Fold Cross-Validation,Modified K-Fold Cross-Validation Tensorboard,Neptune,TensorFlow Profiler to visualization of model performance Distributed Training with TensorFlow ***6.Testing model*** Text Robustness Evaluation Platform https://github.com/textflint/textflint Generally used metrics Always check bias variance tradeoff to know how model is performing Locust Performance Testing ML Serving APIs With Locust https://www.analyticsvidhya.com/blog/2021/06/performance-testing-ml-serving-apis-with-locust/ Model can be overfitting(low bias,high variance),underfitting(high bias,high variance),good fit(low bias,low variance) https://scikit-learn.org/stable/modules/model_evaluation.html https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks KS test to evaluate the separation between class distribution Evaluating the potential return of a model with Lift, Gain, and Decile Analysis 1.Regression task - mean-squared-error, Root-Mean-Squared-Error,mean-absolute error, R², Adjusted R²,Cross-entropy loss,Mean percentage error 2.Classification task-Accuracy,confusion matrix,Precision,Recall,F1 Score,Binary Crossentropy,Categorical Crossentropy,AUC-ROC curve,AUPRC,log loss,Average precision,Mean average precision 3.Reinforcement learning - generally use rewards 4.Incase of machine translation use bleu score 5.Clustering then use External: Adjusted Rand index, Jaccard Score, Purity Score,Rand Index,Mutual Information,V-measure,Fowlkes-Mallows Scores,DBCV Internal:silhouette_score, Davies-Bouldin Index, Dunn Index autoelbow,elbow,Davies-Bouldin Index,Calinski-Harabasz Index https://towardsdatascience.com/performance-metrics-in-machine-learning-part-3-clustering-d69550662dc6 6.Object Detection loss-localization loss,classification loss,Focal Loss,IOU,L2 loss 7.Distance Metrics - Euclidean Distance,Manhattan Distance,Minkowski Distance,Hamming Distance https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa Dimensionality Reduction Metrics - Cumulative Explained Variance,Trustworthiness,Sammon’s Mapping 8.Recommender Systems https://parthchokhra.medium.com/evaluating-recommender-systems-590a7b87afa5 Accuracy Metrics (RMSE, MAE),Top-N Hit Rate RecList: The better way to evaluate recommender systems Similarity metrics : Cosine similarity,Jaccard similarity,Euclidean distance Predictive metrics: MAE,RMSE metric-Built-in metrics, Custom metric without external parameters,Custom metric with external parameters,Subclassing custom metric layer Robustness Gym: Evaluation Toolkit for NLP https://github.com/robustness-gym/robustness-gym https://medium.com/swlh/custom-loss-and-custom-metrics-using-keras-sequential-model-api-d5bcd3a4ff28 loss-Built-in loss, Custom loss without external parameters,Custom loss with external parameters,Subclassing loss layer https://analyticsindiamag.com/all-pytorch-loss-function/ https://analyticsindiamag.com/ultimate-guide-to-loss-functions-in-tensorflow-keras-api-with-python-implementation/ tensorwatch Debugging, monitoring and visualization for Python Machine Learning and Data Science https://github.com/microsoft/tensorwatch Types of Data Drift : Concept drift,Virtual drift,Covariate shift,Prior probability shift,Annotator drift,Data poisoning mitigate the effects of data drift: Regular retraining,Data preprocessing,Data augmentation,Monitoring,Online learning,Domain adaptation,Annotator and data quality control Methods to Detect Drift A) Statistical Approaches,Page-Hinkley method,Kolmogorov-Smirnov Test,Population Stability Index (PSI),Kullback-Leibler (KL) divergence,Jensen-Shannon divergence, Wasserstein Distance B) Model-Based Approach C) Adaptive Sliding Window d)Data visualization tools e)Model performance monitoring f)Drift detection libraries 𝐭𝐨𝐨𝐥𝐬 𝐭𝐨 𝐝𝐞𝐭𝐞𝐜𝐭 𝐦𝐨𝐝𝐞𝐥 𝐝𝐫𝐢𝐟𝐭𝐬 : 𝐰𝐡𝐲𝐥𝐨𝐠𝐬,𝐄𝐯𝐢𝐝𝐞𝐧𝐭𝐥𝐲,𝐀𝐥𝐢𝐛𝐢 𝐃𝐞𝐭𝐞𝐜𝐭 Steps to take when there is an occurrence of drift Check Data Quality, Investigate,Retrain the model,Rebuild the model, Pause the model and Fallback Ways to handle Drift in Production a) Rapidly adapt to concept drift b) Be resistant to noise while distinguishing it from concept drift c) Notice and handle severe drift in model performance. article link https://medium.com/@dummahajan/combating-data-drift-the-fight-for-model-accuracy-2c619ee1e33a Docker and Kubernetes https://towardsdatascience.com/deploy-machine-learning-app-built-using-streamlit-and-pycaret-on-google-kubernetes-engine-fd7e393d99cb simplest way to serve your ML models on Kubernetes https://towardsdatascience.com/the-simplest-way-to-serve-your-ml-models-on-kubernetes-5323a380bf9f ***7.deployment*** https://github.com/piyushpathak03/Model-Deployment Train: one off, batch and real-time/online training Serve: Batch, Realtime (Database Trigger, Pub/Sub, web-service, inApp) Continuously Monitor the Behaviour of Deployed Models https://se-ml.github.io/best_practices/04-monitor_models_prod/ Model Monitoring https://www.kdnuggets.com/2021/03/machine-learning-model-monitoring-checklist.html Automate Model Deployment https://se-ml.github.io/best_practices/04-auto_model_packaging/ Platform as a Service (PaaS),Infrastructure as a Service (IaaS),SaaS (Software as a Service) 3 main approaches of Saving and Reloading an ML Model-Pickle Approach,Joblib Approach,JSON approach https://www.datacamp.com/community/tutorials/pickle-python-tutorial https://github.com/balavenkatesh3322/model_deployment 1.Azure 2.Heroku 3.Amazon Web Services Everything AWS https://app.polymersearch.com/discover/aws 4.Google cloud platform 5.ngrok https://www.youtube.com/watch?v=AkEnjJ5yWV0 Deploy a Machine Learning Model for Free https://www.freecodecamp.org/news/deploy-your-machine-learning-models-for-free/ mlpack is a fast, flexible machine learning library suitable for both data science prototyping and deployment https://numfocus.org/project/mlpack https://github.com/mlpack/mlpack MODEL DEPLOYMENT USING TF SERVING Dockerize https://www.kdnuggets.com/2021/04/dockerize-any-machine-learning-application.html https://pub.towardsai.net/how-to-dockerize-your-data-science-project-a-quick-guide-b6fa2d6a8ba1 bodywork-core MLOps tool for deploying machine learning projects to Kubernetes https://github.com/bodywork-ml/bodywork-core Create ML model inside the docker container https://dev.to/niteshthapliyal/create-ml-model-inside-the-docker-container-3b23 LyftLearn: ML Model Training Infrastructure built on Kubernetes https://eng.lyft.com/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb Model Serving https://neptune.ai/blog/ml-model-serving-best-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-ml-model-serving-best-tools TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines https://www.tensorflow.org/tfx https://theaisummer.com/tfx/?utm_content=163294295&utm_medium=social&utm_source=linkedin&hss_channel=lcp-42461735 torchblaze https://github.com/MLH-Fellowship/torchblaze https://mlh-fellowship.github.io/torchblaze/ ML Aide Manage Machine Learning Lifecycle https://mlaide.com/home https://medium.com/ml-aide/manage-machine-learning-lifecycle-with-ml-aide-dfe7710cbe53 Models visualization using Tensorboard,netron, TensorBoard.dev Python web Frameworks for App Development- Flask,Streamlit,fastapi,fastDeploy,Django,Web2py,Pyramid,CherryPy,Voila,Kivy and Kivymd streamlit,Gradio,mia,opyrator,plotly jupyterdash,h2o wave,dash,gradio,PyWebIO,r shiny,sanic,panel,flask,django,PySimpleGUI,pywebio,autocalc,Mercury,Chitra ,Bokeh,Panel,jupyter Voila with ipywidgets,Panel,dash,Fast Dash,BentoML,Cortex,Seldon,UnionML,Taipy,fastDeploy,Mlflow,Seldon core,tensorflow serving,kserve,torchserve,ray,clearml,mlrun,pymlpipe,FastDeploy,Shiny,Voila,Cog,BentoML,MLflow,PyMLpipe,truss,playtorch,Streamsync,panel,Databutton,plotly,pyscript, Sanic,skops,Mage,sematic,Cog, BentoML,Truss,bentoctl,Banana,Pyramid,Docker,Kubernetes,SageMaker,TensorFlow Serving,Kubeflow,Cortex,Seldon.io,Cortex,TensorFlow Serving,TorchServe,KFServing,Multi Model Server,Triton Inference Server,ForestFlow,Seldon Core,BudgetML,GraphPipe,Hydrosphere,MLEM,Opyrator,Apache PredictionIO,Cortex,Triton Inference Server,ForestFlow,DeepDetect,Seldon Core,Kubeflow,datapane,Pynecone.io,Anvil,h2oai nitro,rest-model-service,Databutton,CherryPy,Anvil,modelbit,Pynecone,modelbit,wagtail,flet,Chainlit,Solara Django models https://www.deploymachinelearning.com/#create-django-models https://www.deploymachinelearning.com/ BentoML https://github.com/bentoml/BentoML UnionML: the easiest way to build and deploy machine learning microservices https://github.com/unionai-oss/unionml panel high-level app and dashboarding solution for Python https://github.com/holoviz/panel sanic https://github.com/sanic-org/sanic Gradio - take input from user https://gradio.app/getting_started Fast Dash https://fastdash.app/ binder - https://mybinder.org/ Netlify https://www.analyticsvidhya.com/blog/2021/04/easily-deploy-your-machine-learning-model-into-a-web-app-netlify/ streamlit https://www.kdnuggets.com/2019/10/write-web-apps-using-simple-python-data-scientists.html https://www.youtube.com/watch?v=iUgNIFrVejc https://blog.streamlit.io/introducing-theming/ Streamlit Flask App from Colab using remoteit and ngrok https://www.youtube.com/watch?v=O2enoygZwl4 Streamlit to databases https://docs.streamlit.io/en/0.83.0/tutorial/databases.html https://github.com/jrieke/best-of-streamlit https://neptune.ai/blog/streamlit-guide-machine-learning?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-streamlit-guide-machine-learning streamlit-ace https://github.com/okld/streamlit-ace https://www.youtube.com/watch?v=Iv2vt-7AYNQ customize the themes of your Streamlit web apps https://www.youtube.com/watch?v=3xJYP_C4KNE https://github.com/khuyentran1401/Data-science/tree/master/applications/pywebio_examples colab_everything Python library to run streamlit, flask, fastapi, etc on google colab https://github.com/Ankur-singh/colab_everything/ dash https://github.com/plotly/dash panel-highcharts https://awesome-panel.org/ https://github.com/marcskovmadsen/panel-highcharts https://github.com/holoviz/panel https://github.com/holoviz/panel opyrator Turns your machine learning code into microservices with web API, interactive GUI, and more https://github.com/ml-tooling/opyrator plotly https://plotly.com/ https://analyticsindiamag.com/how-to-use-plotly-in-colab/ Creating a Machine Learning App with Power BI and PyCaret Streamlit vs. Dash vs. Shiny vs. Voila vs. Flask vs. Jupyter vs django vs PySimpleGUIvs pywebio vs Gradio vs autocalc vs Mercury vs Chitra https://www.datarevenue.com/en-blog/data-dashboarding-streamlit-vs-dash-vs-shiny-vs-voila,pymlpipe,Lightning Apps,Aibro Mercury: easily convert Python notebook to web app and share with others https://github.com/mljar/mercury autocalc https://github.com/kefirbandi/autocalc https://towardsdatascience.com/creating-a-ui-with-ipywidgets-and-autocalc-2ef8ea4cc6c2 Quickly deploy ML WebApps https://ngrok.com/ Chitra https://github.com/gradsflow/chitra Deepnote https://deepnote.com/ https://www.youtube.com/watch?v=0ppptVxgEI8 booklet https://booklet.ai/ https://towardsdatascience.com/building-a-covid-19-project-recommendation-system-4607806923b9 https://analyticsindiamag.com/top-8-python-tools-for-app-development/ Voila This library can turn your Jupyter notebooks into standalone web apps that can be deployed to any cloud platform. https://voila.readthedocs.io/en/stable/ H2O.ai https://www.h2o.ai/blog/data-to-production-ready-models-to-business-apps-in-just-a-few-steps/ PyQt and Tkinter , PySimpleGUI are GUI programming in Python https://github.com/tirthajyoti/DS-with-PySimpleGUI DearPyGui https://github.com/hoffstadt/DearPyGui PySimpleGUI https://github.com/PySimpleGUI/PySimpleGUI Gooey Turn (almost) any Python command line program into a full GUI application with one line https://github.com/chriskiehl/Gooey snapyml Deploy AI Models For Free -http://snapyml.snapy.ai/ BentoML https://github.com/bentoml/BentoML h20 wave-apps https://github.com/h2oai/wave-apps https://h2oai.github.io/wave/docs/installation/ https://h2oai.github.io/wave/ h20 Wave ML (AutoML for Wave Apps) https://h2oai.github.io/wave/blog/ml-release-0.3.0/ fastapi https://towardsdatascience.com/deploying-ml-models-in-production-with-fastapi-and-celery-7063e539a5db FastAPI + Uvicorn https://www.kdnuggets.com/2021/04/deploy-machine-learning-models-to-web.html FastAPI based template https://github.com/99sbr/fastapi-template fastapi-log 0.0.3 https://pypi.org/project/fastapi-log/ testing FastAPI ML APIs with Locust https://locust.io/ https://rubikscode.net/2022/03/21/performance-testing-fastapi-ml-apis-with-locust/ chitra 𝗖𝗿𝗲𝗮𝘁𝗲 𝗔𝗣𝗜 𝗳𝗼𝗿 𝗔𝗻𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 https://github.com/aniketmaurya/chitra PyWebIO Write Interactive Web App in Script Way Using Python https://www.youtube.com/watch?v=vp1ZNapAy6Y https://towardsdatascience.com/pywebio-write-interactive-web-app-in-script-way-using-python-14f50155af4e https://github.com/tirthajyoti/PyWebIO aibro Deploy Machine Learning Models to the Cloud Quickly and Easily https://aipaca.ai/?ref=hackernoon.com https://medium.datadriveninvestor.com/how-to-deploy-machine-learning-models-to-the-cloud-quickly-and-easily-41cca9425c75 Katana https://github.com/shaz13/katana https://katana-demo.herokuapp.com/redoc https://katana-demo.herokuapp.com/docs DS-with-PySimpleGUI https://github.com/tirthajyoti/DS-with-PySimpleGUI pywinauto Windows GUI Automation with Python tkinter to deploy machine learning model-https://analyticsindiamag.com/complete-tutorial-on-tkinter-to-deploy-machine-learning-model/ Tkinter-Designer Create Beautiful Tkinter GUIs by Drag and Drop https://github.com/ParthJadhav/Tkinter-Designer Web-Based GUI (Gradio)- https://analyticsindiamag.com/guide-to-gradio-create-web-based-gui-applications-for-machine-learning/ https://www.gradio.app/ Bamboolib https://medium.com/ai-in-plain-english/bamboolib-a-data-warriors-weapon-9f734f4c2553 web application(dash)- https://dash.plotly.com/ Pyramid web framework https://trypyramid.com/documentation.html Kivy /Kivymd creating an android app https://towardsdatascience.com/pycaret-2-1-is-here-whats-new-4aae6a7f636a Create a Website with AI https://www.bookmark.com/ localhost to globalurl https://ngrok.com/ https://remote.it/ Jupyter Notebook into an interactive dashboard (voila)-https://voila.readthedocs.io/en/stable/ high-level app and dashboarding solution(Panel)-https://panel.holoviz.org/ MaaS Build ML Models As A Service https://www.analyticsvidhya.com/blog/2021/05/maas-build-ml-models-as-a-service/ https://github.com/gradio-app/gradio ***Tensorflow lite:Use of tensorflow lite to reduce size of model*** https://www.tensorflow.org/lite https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android-beta/#0 https://tfhub.dev/s?deployment-format=lite https://www.tensorflow.org/lite/examples https://www.tensorflow.org/lite/microcontrollers https://www.tensorflow.org/lite/models Adventures-in-TensorFlow-Lite https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite coral https://coral.ai/docs/edgetpu/models-intro/ TF Micro and SensiML https://blog.tensorflow.org/2021/05/building-tinyml-application-with-tf-micro-and-sensiml.html six different types of methods: 1) Pruning, Weight sharing Structured Pruning,Unstructured Pruning,Pruning Local,Global Pruning Pruning criteria( Weight magnitude criterion,Gradient magnitude pruning,Global or local pruning, Model Pruning: Remove irrelevant edges and nodes from a network. Three popular types of pruning: Zero pruning Activation pruning Redundancy pruning 3) Quantization ,TensorFlow Quantum, Int8 quantization Post-Training Quantization — Reduce Float16 — Hybrid Quantization — Integer Quantization -dynamic range quantization - Dynamic/Runtime Quantization - Post-Training Static Quantization - Static Quantization-aware Training (QAT) 2. During-Training Quantization 3. Post-Training Pruning 4. Post-Training Clustering 4) Knowledge distillation 5) Parameter sharing 6) Tensor decomposition 7) Linear Transformer,Winograd Transformation 8) Selective attention 9) Low-rank factorisation 10) 3LC https://research.google/pubs/pub47962/ 11) brevitas https://github.com/Xilinx/brevitas/ 12) aimet https://github.com/quic/aimet Structured pruning,Unstructured/semi-structured pruning,Quantization,Distillation,Post Training,Training-Aware,Sparse Transfer AIMET is a library that provides advanced quantization and compression techniques for trained neural network models. https://github.com/quic/aimet Pruning,Nonstructural pruning,Structural pruning,Quantisation-Aware Training,Post-Training Quantisation QKeras: a quantization deep learning library for Tensorflow Keras Model Compression https://github.com/open-mmlab/mmrazor Knowledge Distillation knowledge are categorized into three different types: Response-based knowledge, Feature-based knowledge, and Relation-based knowledge three principal types of methods for training student and teacher models, namely offline, online and self distillation. Distillation library KD_Lib https://github.com/SforAiDl/KD_Lib ibm new tool https://www.zdnet.com/article/ibms-new-tool-lets-developers-add-quantum-computing-power-to-machine-learning/ qiskit-machine-learning https://github.com/Qiskit/qiskit-machine-learning https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplingNeuralNetwork.html compressors https://github.com/elephantmipt/compressors poniard scikit-learn model comparison https://github.com/rxavier/poniard https://rachitsingh.com/deep-learning-model-compression/#quantization model optimization (architecture) TF Lite with iOS, Swift and TF Lite Swift TinyML https://blog.tensorflow.org/2020/08/the-future-of-ml-tiny-and-bright.html tinyml-papers-and-projects This is a list of interesting papers and projects about TinyML https://github.com/gigwegbe/tinyml-papers-and-projects pennylane Python library for differentiable programming of quantum computers https://github.com/PennyLaneAI/pennylane AI Engine for Edge Devices https://github.com/johnolafenwa/deepstack TensorFlow Lite Samples on Unity https://github.com/asus4/tf-lite-unity-sample tflite-support TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile / ioT devices https://github.com/tensorflow/tflite-support Post-training Quantization in TensorFlow Lite https://www.tensorflow.org/lite/performance/post_training_quantization pruning Custom Text Classification on Android using TensorFlow Lite https://www.analyticsvidhya.com/blog/2021/05/custom-text-classification-on-android-using-tensorflow-lite/ aimet advanced quantization and compression techniques for trained neural network models https://github.com/quic/aimet https://github.com/quic/aimet-model-zoo Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications https://github.com/Tencent/PocketFlow leverage of model architecture Federated Learning https://www.analyticsvidhya.com/blog/2021/04/federated-learning-for-beginners/ https://www.tensorflow.org/federated FEDERATED LEARNING(Centralized, Decentralized, Heterogeneous) https://blog.openmined.org/federated-learning-types/ https://aman.ai/primers/ai/federated-learning/ Federated Learning with FEDn https://github.com/scaleoutsystems/fedn plato scalable federated learning research framework https://github.com/TL-System/plato FedNLP: A Research Platform for Federated Learning in Natural Language Processing https://github.com/FedML-AI/FedNLP privacy https://github.com/tensorflow/privacy Differential Privacy https://aman.ai/primers/ai/differential-privacy/ ***Quantization:Use Quantization to reduce size of model*** https://medium.com/qiskit/introducing-qiskit-machine-learning-5f06b6597526 Post Training Quantization Aware Training Quantization TensorFlow Quantum https://www.tensorflow.org/quantum Qiskit Machine Learning https://github.com/Qiskit/qiskit-machine-learning Quantum Machine Learning Quantum Kernels https://github.com/Qiskit/qiskit-machine-learning/blob/master/docs/tutorials/03_quantum_kernel.ipynb IBMs Qiskit,Google’s Cirq,Amazon’s AWS Braket,Microsoft’s Q# and Azure Quantum,Rigetti’s Forest,Xanadu’s Pennylane ***On-Device Machine Learning*** https://developers.google.com/learn/topics/on-device-ml https://www.tensorflow.org/lite/guide/model_maker Core ML for iOS, Tensorflow lite for Android, ML.NET for Windows and ML Kit ***8.Mointoring model*** ***CI CD pipeline used- circleci , jenkins*** ***In real world project use pipeline*** -https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html 1.easy debugging 2.better readability Types of Data Drift Concept drift,Virtual drift,Covariate shift,Prior probability shift,Annotator drift,Data poisoning There are several measures you can take to mitigate the effects of data drift: Regular retraining,Data preprocessing,Data augmentation,Monitoring,Online learning,Domain adaptation,Annotator and data quality control Techniques for Detecting Data Drift There are several techniques currently available for detecting data drift in machine learning: Data visualization tools,Drift detection methods,Data quality control techniques,Drift detection libraries,Auto-ML tools ***BIG DATA: hadoop,apache spark*** ***project structure*** data science project structure https://towardsdatascience.com/automate-your-data-science-project-structure-in-three-easy-steps-277c92328d24 ***research paper***-https://arxiv.org/ ,https://arxiv.org/list/cs.LG/recent, https://www.kaggle.com/Cornell-University/arxiv arXiv.org https://arxiv.org/list/cs.AI/recent https://arxiv.org/list/stat.ML/recent https://arxiv.org/list/cs.CL/recent https://arxiv.org/list/cs.CV/recent https://github.com/amitness/papers-with-video Datasets on arXiv https://medium.com/paperswithcode/datasets-on-arxiv-1a5a8f7bd104 code for research paper https://www.analyticsvidhya.com/blog/2021/06/steal-the-code-ethically-get-better-at-ml-ai-research/ papertalk https://papertalk.org/index connected papers https://www.connectedpapers.com/ Stanford AI Lab Papers and Talks at ICLR 2021 https://ramseyelbasheer.io/2021/05/03/stanford-ai-lab-papers-and-talks-at-iclr-2021/ Semantic Scholar searches: https://www.semanticscholar.org/search?q=%22neural%20networks%22&sort=relevance&ae=false https://www.semanticscholar.org/search?q=%22machine%20learning%22&sort=relevance&ae=false https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false https://www.semanticscholar.org/search?q=%22computer%20vision%22&sort=relevance&ae=false https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false code for Research Papers-https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil Summarise Research Papers - https://www.semanticscholar.org/ Structure Your Data Science Projects https://towardsdatascience.com/structure-your-data-science-projects-6c6c8653c16a ***programming language for data science is Python,R,Julia,Java,Scala,JAVA SCRIPT(Tensorflow.js),etc...*** IDE:jupyter notebook,spyder,pycharm,visual studio 4 Tools for Reproducible Jupyter Notebooks https://towardsdatascience.com/4-tools-for-reproducible-jupyter-notebooks-d7423721bd04 12 Jupyter Notebook Extensions That Will Make Your Life Easier https://towardsdatascience.com/12-jupyter-notebook-extensions-that-will-make-your-life-easier-e0aae0bd181 Coding Tools Powered by AI : GitHub Co-Pilot,Tabnine,AI2SQL,Mutable,MarsXm,Ghostwriter,Stenography,OpenAI Codex,CodeT5,Polycoder,GhostWriter Replit,Seek,AI2SQL,Cody by Sourcegraph,MutableAI,StableCode,DeciCoder,santacoder,Code Llama,Amazon CodeWhisperer,Bagasura ***BEST ONLINE COURSES*** 1.COURSERA 2.UDEMY 3.EDX 4.DATACAMP 5.Udacity 6.https://www.skillbasics.com/ ***BEST YOUTUBE CHANNEL TO FOLLOW*** 1.Krish Naik-https://www.youtube.com/user/krishnaik06 2.Codebasics-https://www.youtube.com/channel/UCh9nVJoWXmFb7sLApWGcLPQ 3.Abhishek thakur-https://www.youtube.com/user/abhisheksvnit 4.AIEngineering-https://www.youtube.com/channel/UCwBs8TLOogwyGd0GxHCp-Dw 5.Ineuron-https://www.youtube.com/channel/UCb1GdqUqArXMQ3RS86lqqOw 6.Ken jee-https://www.youtube.com/c/KenJee1/featured 7.3Blue1Brown-https://www.youtube.com/c/3blue1brown/featured 8.The AI Guy -https://www.youtube.com/channel/UCrydcKaojc44XnuXrfhlV8Q 9.Unfold Data Science-https://www.youtube.com/channel/UCh8IuVJvRdporrHi-I9H7Vw etc... ***BEST BLOGS TO FOLLOW*** https://www.cybrhome.com/topic/data-science-blogs AI Summary https://ai-summary.com/ https://www.datasciencecentral.com/profiles/blog/list https://developer.nvidia.com/blog/?ncid=em-prom-48627 1.Towards data science-https://towardsdatascience.com/ 2.Analyticsvidhya-https://www.analyticsvidhya.com/blog/?utm_source=feed&utm_medium=navbar https://analyticsindiamag.com/ https://www.analyticsinsight.net/ 3.Medium-https://medium.com/ 4.Machinelearningmastery-https://machinelearningmastery.com/blog/ 5.ML+ -https://www.machinelearningplus.com/ 6.analyticsinsight https://www.analyticsinsight.net/category/latest-news/ https://www.analyticsinsight.net/ 7.KDnuggets https://www.kdnuggets.com/ https://www.kdnuggets.com/news/index.html 8.Artificial Intelligence Database https://www.wired.com/category/artificial-intelligence/?verso=true https://machinelearningknowledge.ai/ https://github.com/rushter/data-science-blogs https://www.datamuni.com/ https://blog.ml.cmu.edu/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow https://www.amazon.science/blog?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine+learning+blog&utm_content=machine+learning+blog&f0=0000016e-2ff1-d205-a5ef-aff9651e0000&s=0 https://distill.pub/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow https://ai.googleblog.com/search/label/Machine%20Learning?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow https://neptune.ai/blog?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine+learning+blog&utm_content=machine+learning+blog https://bair.berkeley.edu/blog/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow https://deepmind.com/research?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow&filters=%7B%22category%22:%5B%22Research%22%5D%7D https://ai.facebook.com/blog/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow https://becominghuman.ai/top-25-ai-and-machine-learning-blogs-for-data-scientists-9f121bcfd9a2 https://medium.com/towards-artificial-intelligence/best-machine-learning-blogs-to-follow-ml-research-ai-3994e01967f9 ***BEST RESOURCES*** https://amitness.com/toolbox/ https://khuyentran1401.github.io/Data-science/ https://github.com/ml-tooling/best-of-ml-python https://github.com/ml-tooling/best-of-ml-python#machine-learning-frameworks http://dfkoz.com/ai-data-landscape/ https://landscape.lfai.foundation/ https://towardsdatascience.com/data-science-tools-f16ecd91c95d https://mathdatasimplified.com/ https://github.com/neomatrix369/awesome-ai-ml-dl https://amitness.com/ https://postsyoumighthavemissed.com/search/ 1.paperswithcode-https://paperswithcode.com/methods https://www.paperswithcode.com/datasets paperswithcode-client https://github.com/paperswithcode/paperswithcode-client https://paperswithcode.com/lib/torchvision https://www.connectedpapers.com/main/4f2eda8077dc7a69bb2b4e0a1a086cf054adb3f9/EfficientNet-Rethinking-Model-Scaling-for-Convolutional-Neural-Networks/graph 2.madewithml-https://madewithml.com/topics/ https://madewithml.com/courses/applied-ml-in-production/ https://github.com/GokuMohandas/applied-ml modelzoo https://modelzoo.co/ Weights & Biases- https://wandb.ai/gallery sotabench-https://sotabench.com/ 3.Deep learning-https://course.fullstackdeeplearning.com/#course-content 4.pytorch deep learning-https://atcold.github.io/pytorch-Deep-Learning/ PYTORCH HUB https://pytorch.org/hub/ https://pytorch.org/hub/research-models 5.https://papers.labml.ai/papers/daily https://42papers.com/ https://www.kdnuggets.com/2019/08/pytorch-cheat-sheet-beginners.html https://www.kdnuggets.com/2019/04/nlp-pytorch.html https://www.kdnuggets.com/2019/08/9-tips-training-lightning-fast-neural-networks-pytorch.html fairscale PyTorch extensions for high performance and large scale training https://github.com/facebookresearch/fairscale PyTorch Lightning-https://github.com/PyTorchLightning/pytorch-lightning https://www.kdnuggets.com/2020/11/deploy-pytorch-lightning-models-production.html https://pytorch-lightning.medium.com/lightning-flash-0-3-new-tasks-visualization-tools-data-pipeline-and-flash-registry-api-1e236ba9530 PYTORCH - https://pytorch.org/ https://pytorch.org/ecosystem/ https://pytorch.org/tutorials/ https://pytorch.org/docs/stable/index.html https://github.com/pytorch/pytorch PYTORCH Lightning https://pytorchlightning.ai/community#projects https://seannaren.medium.com/introducing-pytorch-lightning-sharded-train-sota-models-with-half-the-memory-7bcc8b4484f2 ort Accelerate PyTorch models with ONNX Runtime https://github.com/pytorch/ort lightning-flash https://github.com/PyTorchLightning/lightning-flash https://pytorch-lightning.medium.com/introducing-lightning-flash-the-fastest-way-to-get-started-with-deep-learning-202f196b3b98 torchflare easy-to-use PyTorch Framework https://github.com/Atharva-Phatak/torchflare Lightning Bolts collection of well established, SOTA models and components https://github.com/PyTorchLightning/lightning-bolts Sharded: A New Technique To Double The Size Of PyTorch Models https://towardsdatascience.com/sharded-a-new-technique-to-double-the-size-of-pytorch-models-3af057466dba 𝗢𝗽𝗮𝗰𝘂𝘀 (𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗿𝗶𝘃𝗮𝗰𝘆)-https://opacus.ai/ light-face-detection https://github.com/borhanMorphy/light-face-detection DALLE-pytorch https://github.com/lucidrains/DALLE-pytorch PyTorch JIT -https://lernapparat.de/jit-optimization-intro/ jax- https://github.com/google/jax incubator-mxnet - https://github.com/apache/incubator-mxnet ignite-https://github.com/pytorch/ignite fastText - https://github.com/facebookresearch/fastText rapidminer-https://rapidminer.com/ 5.deep-learning-drizzle-https://deep-learning-drizzle.github.io/ https://deep-learning-drizzle.github.io/index.html 6.Fastaibook-https://github.com/fastai/fastbook , https://course.fast.ai/ https://www.fast.ai/2019/07/08/fastai-nlp/ https://www.fast.ai/2020/08/21/fastai2-launch/ neptune.ai-https://docs.neptune.ai/index.html Dive into Deep Learning http://d2l.ai/ 7.TopDeepLearning-https://github.com/aymericdamien/TopDeepLearning 8.NLP-progress-https://github.com/sebastianruder/NLP-progress 9.EasyOCR,textract,pytesseract,tesserocr,Amazon textract,TabulaPy, pyzbar,pyocr,OCR With Detectron2,PymuPDF,Camelot,keras ocr,Keras CRNN,PDFTableExtract(by PyPDF2),tesseract-ocr,PyMuPDF,pyocr,Apache Tika,pdfPlumber,PDFMiner,PyPDF2,pdfMiner3,pdf2image,pdfquery,TextOCR,keras-CTPN,pytorch-CTPN,ocr.pytorch,layout-parser,tabula,Spark OCR,mmocr,Amazon Rekognition ,Amazon Textract,Azure OCR, Google OCR,PaddleOCR,TrOCR,MMOCR,awesome OCR,Paddle OCR,OCRmyPDF,calamari, attention ocr,Mozart,pdftabextract,Doc2Text,OpenCV’s EAST,deepdoctection,EAST text detector,slate3k,textract,CRAFT-pytorch,ocr donut,LOGOS ocr, ocrpy,docquery,Parsr,DocuQuery,LayoutLM,docTR,docquery,CascadeTabNet,OpenCV,OCRopus,Kraken,OCRmypdf,MMOCR,PPOCR,Keras-OCR,MultiOcr,TrOCR,docTR,surya OCR,Bhashini,OCRopus,Kraken Processing documents as Text: extract text with PyPDF2, extract tables with Camelot or TabulaPy, extract figures with PyMuPDF. Converting documents into Image (OCR): conversion with pdf2image, extract data with PyTesseract plus many other supporting libraries, or just LayoutParser. OCR toolbox from Davar-Lab https://github.com/hikopensource/davar-lab-ocr To pdf: python-pdfkit,wkhtmltopdf,FPDF 10.Awesome-pytorch-list-https://github.com/bharathgs/Awesome-pytorch-list https://shivanandroy.com/awesome-nlp-resources/ 11.free-data-science-books-https://github.com/chaconnewu/free-data-science-books 12.arcgis-https://github.com/Esri/arcgis-python-api https://geemap.org/ 13.data-science-ipython-notebooks-https://github.com/donnemartin/data-science-ipython-notebooks 14.julia-https://github.com/JuliaLang/julia , https://docs.julialang.org/en/v1/ 15.google-research-https://github.com/google-research/google-research 16.reinforcement-learning-https://github.com/dennybritz/reinforcement-learning 17.keras-applications-https://github.com/keras-team/keras-applications , https://github.com/keras-team/keras https://keras.io/examples/ 18.opencv-https://github.com/opencv/opencv 19.transformers-https://github.com/huggingface/transformers 20.code implementations for research papers-https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil 21.regarding satellite images - Geo AI,Arcgis,geemap ersi arcgis-https://www.esri.com/en-us/arcgis/about-arcgis/overview earthcube-https://www.earthcube.eu/ geemap-https://geemap.org/ 22.Monk_Object_Detection-https://github.com/Tessellate-Imaging/Monk_Object_Detection https://github.com/Tessellate-Imaging/monk_v1 https://analyticsindiamag.com/build-computer-vision-applications-with-few-lines-of-code-using-monk-ai/ pyradox https://github.com/Ritvik19/pyradox 23.NLP-progress - https://github.com/sebastianruder/NLP-progress 24.interview-question-data-science-https://github.com/iNeuronai/interview-question-data-science- 27.Tool for visualizing attention in the Transformer model-https://github.com/jessevig/bertviz 28.TransCoder-https://github.com/facebookresearch/TransCoder 29.Tessellate-Imaging-https://github.com/Tessellate-Imaging/monk_v1 Monk_Object_Detection-https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials- https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 30.Machine-Learning-with-Python-https://github.com/tirthajyoti/Machine-Learning-with-Python 31.huggingface contain almost all nlp pretrained model and all tasks related to nlp field https://huggingface.co/course/chapter0?fw=pt https://huggingface.co/models https://www.kdnuggets.com/2021/02/hugging-face-transformer-basics.html https://huggingface.co/modelsz https://github.com/huggingface https://github.com/huggingface/transformers https://huggingface.co/transformers/ https://huggingface.co/transformers/master/ https://github.com/huggingface/tokenizers hugging face spaces https://huggingface.co/spaces Hugging Face pipelines https://towardsdatascience.com/effortless-nlp-using-pre-trained-hugging-face-pipelines-with-just-3-lines-of-code-a4788d95754f Fine-tuning pretrained NLP models with Huggingface’s Trainer https://towardsdatascience.com/fine-tuning-pretrained-nlp-models-with-huggingfaces-trainer-6326a4456e7b Mixing Hugging Face Models with Gradio 2.0 https://gradio.app/blog/using-huggingface-models https://huggingface.co/blog/gradio ktrain https://github.com/amaiya/ktrain Top 6 Alternatives To Hugging Face https://analyticsindiamag.com/top-6-alternatives-to-hugging-face/ 32.multi-task-NLP-https://github.com/hellohaptik/multi-task-NLP 33.gpt-2 - https://github.com/openai/gpt-2 34.Powerful and efficient Computer Vision Annotation Tool (CVAT)-https://github.com/openvinotoolkit/cvat, https://github.com/abreheret/PixelAnnotationTool https://github.com/UniversalDataTool/universal-data-tool http://www.robots.ox.ac.uk/~vgg/software/via/ 36.awesome Data Science-https://github.com/academic/awesome-datascience 39.Super Duper NLP Repo-https://notebooks.quantumstat.com/ https://models.quantumstat.com/ https://miro.com/app/board/o9J_kqndLls=/ https://datasets.quantumstat.com/ https://index.quantumstat.com/ https://notebooks.quantumstat.com/?utm_campaign=NLP%20News&utm_medium=email&utm_source=Revue%20newsletter 40.papers summarizing the advances in the field-https://github.com/eugeneyan/ml-surveys 41.deep-translator-https://github.com/nidhaloff/deep-translator 44.ipython-sql-https://github.com/catherinedevlin/ipython-sql 45.libra-https://github.com/Palashio/libra 46.opencv-https://github.com/opencv/opencv 47.learnopencv-https://github.com/spmallick/learnopencv , https://www.learnopencv.com/ 48.math is fun-https://www.mathsisfun.com/ , https://pabloinsente.github.io/intro-linear-algebra, https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/ 49.DEEP LEARNING WITH PYTORCH: A 60 MINUTE BLITZ - https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html 50.https://data-flair.training/blogs/ https://data-flair.training/blogs/python-tutorials-home/ https://data-flair.training/blogs/hadoop-tutorials-home/ https://data-flair.training/blogs/spark-tutorials-home/ https://data-flair.training/blogs/tableau-tutorials-home/ https://data-flair.training/blogs/data-science-tutorials-home/ Spark Release 3.0.1-https://spark.apache.org/releases/spark-release-3-0-1.html https://neptune.ai/blog/apache-spark-tutorial Koalas on Apache Spark - Pandas API https://www.youtube.com/watch?v=kOtAMiMe1JY&t=482s https://koalas.readthedocs.io/en/latest/ mllib https://spark.apache.org/docs/2.0.0/api/python/pyspark.mllib.html https://spark.apache.org/docs/2.0.0/api/python/index.html https://data-flair.training/blogs/spark-tutorial/ Spark Core,Spark SQL,Spark Streaming,Spark MLlib,Spark GraphX,etc... Machine Learning with Optimus on Apache Spark https://www.kdnuggets.com/2017/11/machine-learning-with-optimus.html BigDL: Distributed Deep Learning Framework for Apache Spark https://github.com/intel-analytics/BigDL 51.for more cheatsheets-https://github.com/FavioVazquez/ds-cheatsheets , https://medium.com/swlh/the-ultimate-cheat-sheet-for-data-scientists-d1e247b6a60c https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning 52.text2emotion-https://pypi.org/project/text2emotion/ 53.ExploriPy-https://analyticsindiamag.com/hands-on-tutorial-on-exploripy-effortless-target-based-eda-tool/ 54.TCN-https://github.com/philipperemy/keras-tcn 56.earthengine-py-notebooks-https://github.com/giswqs/earthengine-py-notebooks 58.numerical-linear-algebra -https://github.com/fastai/numerical-linear-algebra 61.chatbot- from scratch,google dialogflow,rasa nlu,azure luis, Azure Bot Service,chatterbot,Amazon lex,Wit.ai,Luis.ai,IBM Watson,Parrot etc... Chatterbot,Botkit,BotPress,Bottender,IBM Watson,Microsoft bot Framework,Pandorabots,RASA Stack,Pandorabots,BlenderBot3,DeepPavlov,OpenDialogTock,Wit.ai, Pandorabots,Proto AIC,HubSpot Chatbot Builder,Intercom,Zendesk,Freshworks,Botsify,Tidio,Infobip,OpenChat ChatGPT openai chatboat and search engine,meta ChatLLaMA ,VisualChatGPT,ViperGPT,GPT-4,AutoGPT,babyagi,ChaosGPT,Agentgpt,MiniGPT-4,GPT4 All ,BabyAGI and Auto-GPT,Dolly,Camel,claude2,bing,Code Interpreter,Anthropic's,WizardCoder Bard google chatboat and search engine,PALM API,OpenChatKit: Open-Source ChatGPT Alternative meta LLaMA,LLaMA-v2,Alpaca 7B,h2o-llmstudio,StableLM,HuggingChat Ernie bot,Baidu chatbot,Claude,Alpaca,ChatGLM,Bloomberg-GPT,Vicuna,StackLLaMA,h2o-llmstudio,Claude 2,Perplexity Ai,FreeWilly1,FreeWilly2,Falcon,Dolly,Guanaco,BloomZ,Alpaca,OpenChatKit,GPT4ALL,Vicuna,Flan-T5,FalconLite ,StableBeluga2,Tongyi Qianwen no code chatbots https://juji.io/ https://github.com/fendouai/Awesome-Chatbot https://medium.com/nerd-for-tech/make-money-building-a-fast-powerful-chatbot-in-10-minutes-using-nltk-91038e15ab17 https://www.analyticsinsight.net/category/chatbots/ https://www.promaticsindia.com/blog/here-are-the-most-popular-chatbot-development-frameworks/ https://neptune.ai/blog/building-machine-learning-chatbots-platforms-and-applications?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-building-machine-learning-chatbots-platforms-and-applications https://blog.ubisend.com/optimise-chatbots/chatbot-training-data OpenChat: Open Source Chatting Framework for Generative Models https://analyticsindiamag.com/a-brief-overview-of-openchat-open-source-chatting-framework-for-generative-models/ 62. No Code Machine Learning / Deep Learning https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/ https://www.pye.ai/2021/06/01/2021-list-of-top-data-science-platforms-end-to-end-machine-learning/ https://serokell.io/blog/top-no-code-platforms https://www.nanalyze.com/2021/04/no-code-platforms-machine-learning/ Akkio, Obviously.ai, DataRobot, Levity, Clarifai, Teachable Machines, Lobe,pimer,DynaBench,APAflow,Runway AI,Obviously AI,CreateML,MakeML,Fritz AI,MonkeyLearn,Nanonets,SuperAnnotate,CausaLens,Levity,Clarifai,BigML,Teachable Machine,actable,Bonsai,labelsleuth,Cooka,oracle AutoML,EdgeImpulse,Mantium AI,Sway,Graphite,DataRobot,Graphite Note,Levity,MakeML,MonkeyLearn,Noogata,Obviously.ai,Pecan,RapidMiner,RunwayML,SuperAnnotate,KNIME,DashB.ai,NoCode-ML,BMW-TensorFlow-Training-GUI,Akkio Teachable Machine-https://teachablemachine.withgoogle.com/ Vertex AI https://cloud.google.com/vertex-ai/docs/start/automl-users Microsoft Lobe -https://lobe.ai/ Ludwig https://github.com/ludwig-ai/ludwig WEKA - https://www.cs.waikato.ac.nz/ml/weka/ autoweka Create ML https://developer.apple.com/documentation/createml APAflow https://apaflow.com/?utm_medium=social&utm_source=linkedin&utm_campaign=postfity&utm_content=postfity0b527 https://apaflow.com/ Monk_Gui-https://github.com/Tessellate-Imaging/Monk_Gui FlashML https://www.flash-ml.com/ JADBio’s https://www.jadbio.com/ JOHN SNOW LABS https://www.johnsnowlabs.com/models-training-and-active-learning-in-john-snow-labs-annotation-lab/ igel https://github.com/nidhaloff/igel BRYTER https://bryter.com Ushur https://ushur.com Accern https://accern.com Signzy https://signzy.com Runway https://runwayml.com Fritz AI https://www.fritz.ai BigML, Inc https://bigml.com MyDataModels https://lnkd.in/eejjDbM MonkeyLearn https://monkeylearn.com Levity https://levity.ai Nanonets https://nanonets.com obviously https://www.obviously.ai/ machine learning straight from Microsoft Excel https://venturebeat.com/2020/12/30/you-dont-code-do-machine-learning-straight-from-microsoft-excel/ ENNUI-https://math.mit.edu/ennui/ https://github.com/martinjm97/ENNUI https://www.youtube.com/watch?v=4VRC5k0Qs2w Knime https://www.knime.com/ Accord.net http://accord-framework.net/ DeepDev https://realmichaelye.github.io/DeepDev/deepdev.tech%20-%20Landing%20Page/ https://github.com/realmichaelye/DeepDev H2O Driverless AI https://www.h2o.ai/products/h2o-driverless-ai/ Oracle AutoML https://medium.com/nerd-for-tech/oracles-automl-what-it-is-and-how-it-works-12e09a832c2 https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/user_guide/overview/overview.html Rapid Miner https://rapidminer.com/ opennn https://www.opennn.net/ datarobot https://www.datarobot.com/ dataiku https://www.dataiku.com/product/get-started/ orange https://orange.biolab.si/ Databricks AutoML Automate Machine Learning using Databricks AutoML https://pub.towardsai.net/automate-machine-learning-using-databricks-automl-a-glass-box-approach-and-mlflow-2543a8143687 OpenBlender https://openblender.io/#/welcome https://analyticsindiamag.com/how-to-use-openblender-the-leading-data-blending-tool/ create neural networks with one line of code https://github.com/PraneetNeuro/nnio.l AWS SageMaker AutoPilot https://aws.amazon.com/sagemaker/autopilot/ Machine Learning in JUST ONE LINE OF CODE libra https://github.com/Palashio/libra/ https://www.youtube.com/watch?v=N_T_ljj5vc4 perceptilabs https://towardsdatascience.com/easy-model-building-with-perceptilabs-interactive-tensorflowvisualization-gui-834d5bb3c973 64.tensorflow development-https://blog.tensorflow.org/ TensorFlow Hub (trained ready-to-deploy machine learning models in one place) - https://tfhub.dev/ CrypTFlow: An End-to-end System for Secure TensorFlow Inference https://github.com/mpc-msri/EzPC https://pratik-bhatu.medium.com/privacy-preserving-machine-learning-for-healthcare-using-cryptflow-cc6c379fbab7 TensorBoard.dev - https://tensorboard.dev/ tutorials-https://www.tensorflow.org/tutorials https://www.tensorflow.org/guide TensorFlow Graphics - https://www.tensorflow.org/graphics Lattice-https://www.tensorflow.org/lattice TensorFlow Probability-https://www.tensorflow.org/probability TensorFlow Privacy- tensorflow-privacy https://developers.google.com/learn/topics/on-device-ml https://www.tensorflow.org/lite/guide/model_maker https://tfhub.dev/ https://www.tensorflow.org/cloud 63.Data Science in the Cloud-Amazon SageMaker,Amazon Lex,Amazon Rekognition,Azure Machine Learning (Azure ML) Services,Azure Service Bot framework,Google Cloud AutoML 64.platforms to build and deploy ML models -Uber has Michelangelo,Google has TFX,Databricks has MLFlow,Amazon Web Services (AWS) has Sagemaker 66.ML from scratch-https://dafriedman97.github.io/mlbook/content/introduction.html https://aihubprojects.com/machine-learning-from-scratch-python/ https://github.com/python-engineer/MLfromscratch https://www.youtube.com/watch?v=rLOyrWV8gmA https://www.datasciencecentral.com/profiles/blogs/a-complete-tutorial-to-learn-data-science-with-python-from https://medium.com/@mattybv3/learn-data-science-from-scratch-curriculum-with-20-free-online-courses-8cff96d6cbe5 67.turn-on visual training for most popular ML algorithms https://github.com/lucko515/ml_tutor https://pypi.org/project/ml-tutor/ 68.mlcourse.ai is a free online- https://mlcourse.ai/ 72.R for Data Science-https://r4ds.had.co.nz/ ,Fundamentals of Data Visualization-https://clauswilke.com/dataviz/ 74.machine learning in JavaScript-https://www.tensorflow.org/js https://www.tensorflow.org/js/models https://tensorflow-js-object-detection.glitch.me/ TensorFlow.jl Julia with TensorFlow https://malmaud.github.io/tfdocs/ https://malmaud.github.io/TensorFlow.jl/latest/tutorial.html Sonnet is a library built on top of TensorFlow 2 https://github.com/deepmind/sonnet TensorFlow Federated (TFF) ( facilitate open research and experimentation with Federated Learning)-https://www.tensorflow.org/federated TFX is an end-to-end platform for deploying production ML pipelines https://www.tensorflow.org/tfx https://github.com/tensorflow/tfx https://analyticsindiamag.com/guide-to-tensorflow-extendedtfx-end-to-end-platform-for-deploying-production-ml-pipelines/ Federated Learning -https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification Neural Structured Learning-https://www.tensorflow.org/neural_structured_learning/tutorials/graph_keras_mlp_cora Responsible AI-https://www.tensorflow.org/resources/responsible-ai https://www.tensorflow.org/graphics 75.free list of AI/ Machine Learning Resources/Courses-https://www.marktechpost.com/free-resources/ https://github.com/kabartay/OpenUnivCourses Open ML University https://curriculum.openmlu.com/ https://www.kdnuggets.com/2018/11/10-free-must-see-courses-machine-learning-data-science.html https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html https://www.theinsaneapp.com/2020/11/free-machine-learning-data-science-and-python-books.html 65 Machine Learning and Data books for free- https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189 https://www.deeplearningbook.org/ http://d2l.ai/ https://www.theinsaneapp.com/2020/12/download-free-machine-learning-books.html https://www.datasciencecentral.com/profiles/blogs/free-500-page-book-on-applications-of-deep-neural-networks-1 https://github.com/jeffheaton/t81_558_deep_learning https://www.theinsaneapp.com/2020/12/free-data-science-books-pdf.html https://www.datasciencecentral.com/profiles/blogs/free-500-page-book-on-applications-of-deep-neural-networks-1 https://github.com/chaconnewu/free-data-science-books https://www.kdnuggets.com/2020/03/24-best-free-books-understand-machine-learning.html https://www.kdnuggets.com/2020/12/15-free-data-science-machine-learning-statistics-ebooks-2021.html http://introtodeeplearning.com/ https://www.theinsaneapp.com/2020/12/free-data-science-books-pdf.html http://d2l.ai/index.html https://www.kdnuggets.com/2020/09/best-free-data-science-ebooks-2020-update.html https://www.youtube.com/playlist?app=desktop&list=PLypiXJdtIca5ElZMWHl4HMeyle2AzUgVB https://mit6874.github.io/ 79.For practice -https://www.confetti.ai/exams 80.Yellowbrick-https://towardsdatascience.com/introduction-to-yellowbrick-a-python-library-to-explain-the-prediction-of-your-machine-learning-d63ecee10ecc 81.Mathematics of Machine Learning,deep learning-https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://github.com/hrnbot/Basic-Mathematics-for-Machine-Learning https://towardsdatascience.com/the-roadmap-of-mathematics-for-deep-learning-357b3db8569b https://medium.com/towards-artificial-intelligence/basic-linear-algebra-for-deep-learning-and-machine-learning-ml-python-tutorial-444e23db3e9e https://www.kdnuggets.com/2020/02/free-mathematics-courses-data-science-machine-learning.html https://towardsai.net/p/data-science/how-much-math-do-i-need-in-data-science-d05d83f8cb19 https://www.mltut.com/how-to-learn-math-for-machine-learning-step-by-step-guide/ https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks# https://www.datasciencecentral.com/profiles/blogs/free-online-book-machine-learning-from-scratch https://hadrienj.github.io/posts/Essential-Math-for-Data-Science-Introduction_to_matrices_and_matrix_product/?utm_source=linkedin&utm_medium=social&utm_campaign=linkedin_matrices https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a https://github.com/jonkrohn/ML-foundations https://ocw.mit.edu/resources/res-18-001-calculus-online-textbook-spring-2005/textbook/ 82.Googleai-https://ai.google/education 83.ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions PyBrain is a modular Machine Learning Library for Python 84.Best Online Courses for Machine Learning and Data Science-https://www.mltut.com/best-online-courses-for-machine-learning-and-data-science/ Comprehensive Project Based Data Science Curriculum https://julienbeaulieu.github.io/2019/09/25/comprehensive-project-based-data-science-curriculum/ AI Expert Roadmap-https://i.am.ai/roadmap/#data-science-roadmap 86.Yann LeCun’s Deep Learning Course at CDS-https://cds.nyu.edu/deep-learning/ https://atcold.github.io/pytorch-Deep-Learning/ https://atcold.github.io/pytorch-Deep-Learning/ https://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml 88.Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/ 91.AudioFeaturizer when deal with audio data- https://pypi.org/project/AudioFeaturizer/ liborsa library https://librosa.org/doc/latest/index.html MAGENTA-https://magenta.tensorflow.org/ pydub https://github.com/jiaaro/pydub DDSP: Differentiable Digital Signal Processing https://github.com/magenta/ddsp https://analyticsindiamag.com/guide-to-differentiable-digital-signal-processing-ddsp-library-with-python-code/ 92.Palladium-https://palladium.readthedocs.io/en/latest/ 94.Facebook Open Sourced New Frameworks to Advance Deep Learning Research https://www.kdnuggets.com/2020/11/facebook-open-source-frameworks-advance-deep-learning-research.html 95.Software Engineering for Machine Learning https://github.com/SE-ML/awesome-seml 96.Atlas web-based dashboard -https://www.atlas.dessa.com/ 97.Pytest (test code) https://docs.pytest.org/en/latest/index.html (test code) 98.keras- https://keras.io/ https://keras.io/api/ https://keras.io/examples/ 99.High-Performance Jupyter Notebook - BlazingSQL Notebooks https://blazingsql.com/notebooks jupyter-tabnine https://github.com/wenmin-wu/jupyter-tabnine 101.Kubeflow Machine Learning Toolkit for Kubernetes https://www.kubeflow.org/ 102.Daily AI updates to your inbox- https://sago-ai.news/#/ 103.Three API styles - Sequential Model,functional API,Model subclassing 104.Deep Learning Toolkit for Medical Image Analysis -https://github.com/DLTK/DLTK 3 Python Packages for Machine Learning Validation Evidently,Deepchecks,TensorFlow-Data-Validation 106.Explainability : Model-Specific explainability(Explainability method is strictly relevant to specific model) ,Model-Agnostic explainability ( Explanation to any type model),Model-Centric explainability(most Explanation methods are Model-Centric, as these methods are used to explain how the features and target values are being adjusted),Data-Centric explainability(these methods are used to understand the nature of the data) Interpret The ML Model https://towardsdatascience.com/explainable-artificial-intelligence-part-3-hands-on-machine-learning-model-interpretation-e8ebe5afc608 https://christophm.github.io/interpretable-ml-book/ https://www.kaggle.com/getting-started/209632 https://ex.pegg.io/ https://neptune.ai/blog/explainability-auditability-ml-definitions-techniques-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-explainability-auditability-ml-definitions-techniques-tools shap,lime,Shapash,webshap,ELI5,InterpretML,Concept Relevance Propagation,OmniXAI,Treeinterpreter,Dalex,Eli5,Yellowbrick,Mlxtend,PDPBox,InterpretML,Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) Plots, Accumulated Local Effects (ALE) Curves and Permutation Importance,Casual shap values,Integrated Gradients,Anchors,Feature importance/attribution,SmoothGrad,DeepLIFT,GradientExplainer,decision tree surrogates,Permutation feature importance, xplique,ANCHORS,Permutation Importance,Morris Sensitivity Analysis,Contrastive Explanation Method (CEM),Counterfactual Instances,Global Interpretation via Recursive,Partitioning (GIRP),Protodash,Scalable Bayesian Rule Lists,Tree Surrogates,Explainable Boosting Machine (EBM),DALEX,ALIBI,DiCE,Explainerdashboards,TCAV,PiML,Xplique,Explainer_dashboard,InterpretML,tcav,FeatureImportance,Layerwise Propagation,Surrogate,Explainer Partial Dependence,solas,ferret,Integrated Gradients,DeepLift,Explainable Boosting Machine,Saliency maps,TCAV,Distillation,Counterfactual,interpretML,pdpbox,PyALE,interpret, Fast interpretable,greedy-tree sums,interpretml,imodels,ferret,Counterfactual explanations ,Layerwise Relevance Propagation,Integrated Gradients (IG),Deep LIFT, Saliency,Feature Ablation,Occlusion,captum,Accumulated Local Effects,Anchors,Integrated Gradients,Counterfactuals,GradientShap,FastTreeShap,DeepLift,DeepLiftShap,IntegratedGradients,LayerConductance,NeuronConductance,NoiseTunnel,InterpretML,ALIBI DiCE,interpret-text,aix360,OmniXAI,BreakDown,interpret-text,iml (Interpretable Machine Learning),OmniXAI,Explainerdashboard,InterpretML,ELI5,Netron,DoWhy,CausalNex,explainerdashboard,fairlearn,arviz,Explainability,iNNvestigate,Model Analysis,Permutation feature importance,Partial dependency plots,TE2Rules OmniXAI: A Library for eXplainable AI https://github.com/salesforce/OmniXAI Xplique is a Neural Networks Explainability Toolbox https://github.com/deel-ai/xplique/ Ethical-AI Toolkits https://murat-durmus.medium.com/an-brief-overview-of-some-ethical-ai-toolkits-712afe9f3b3a ferret python package for benchmarking interpretability techniques https://github.com/g8a9/ferret explaining machine learning models https://github.com/SeldonIO/alibi https://github.com/salesforce/OmniXAI https://github.com/SeldonIO/alibi Awesome-explainable-AI https://ex.pegg.io/ tf-explain https://github.com/sicara/tf-explain imodels https://github.com/csinva/imodels lime(explain black box models)- https://lime-ml.readthedocs.io/en/latest/ https://towardsdatascience.com/interpreting-image-classification-model-with-lime-1e7064a2f2e5 SHAP https://medium.com/towards-artificial-intelligence/explain-your-machine-learning-predictions-with-kernel-shap-kernel-explainer-fed56b9250b8 SHAP and Kernel SHAP,TreeSHAP,shparkley,Shparkley,Deep SHAP,TimeSHAP,PySpark-SHAP,GPUTreeSHAP,FastTreeSHAP: Accelerating SHAP value computation for trees https://github.com/linkedin/fasttreeshap https://github.com/slundberg/shap https://www.kdnuggets.com/2020/01/explaining-black-box-models-ensemble-deep-learning-lime-shap.html https://analyticsindiamag.com/hands-on-guide-to-interpret-machine-learning-with-shap/ fastshap https://github.com/bgreenwell/fastshap xplique https://github.com/deel-ai/xplique?utm_source=pocket_mylist Shapash makes Machine Learning models transparent and understandable by everyone https://github.com/MAIF/shapash https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html Captum is a model interpretability and understanding library for PyTorch https://github.com/pytorch/captum Explainable AI https://ex.pegg.io/ Explainable AI dashboards https://github.com/oegedijk/explainerdashboard https://www.youtube.com/watch?v=ZgypAMRcmw8 interpret https://github.com/interpretml/interpret mlxtend's http://rasbt.github.io/mlxtend/ imodels Interpretable ML package https://github.com/csinva/imodels Quantus eXplainable AI toolkit https://github.com/understandable-machine-intelligence-lab/quantus DiCE Generate Diverse Counterfactual Explanations for any machine learning model. https://github.com/interpretml/DiCE tcav https://github.com/tensorflow/tcav yellowbrick https://www.scikit-yb.org/en/latest/quickstart.html Language Interpretability Tool https://github.com/pair-code/lit https://ai.googleblog.com/2020/11/the-language-interpretability-tool-lit.html Transformers Interpret https://towardsdatascience.com/introducing-transformers-interpret-explainable-ai-for-transformers-890a403a9470 https://github.com/cdpierse/transformers-interpret treeinterpreter https://github.com/andosa/treeinterpreter Adversarial Explainable AI https://github.com/hbaniecki/adversarial-explainable-ai https://medium.com/responsibleml/adversarial-attacks-on-explainable-ai-f65d41e83c5f Captum Model Interpretability for PyTorch https://captum.ai/ https://github.com/pytorch/captum ecco https://github.com/jalammar/ecco https://jalammar.github.io/explaining-transformers/ https://www.eccox.io/ dalex https://pypi.org/project/dalex/ https://blog.learningdollars.com/2021/01/02/ai-in-medical-diagnosis/ https://www.kdnuggets.com/2020/11/dalex-explain-tensorflow-model.html google AI Explanations for AI Platform https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview?utm_source=youtube&utm_medium=Unpaidsocial&utm_campaign=guo-20200423-Intro-Aiexp eli5 https://eli5.readthedocs.io/en/latest/ Integrated-Gradients https://github.com/ankurtaly/Integrated-Gradients xplique https://github.com/deel-ai/xplique/ TabNet: Attentive Interpretable Tabular Learning https://github.com/dreamquark-ai/tabnet skater https://oracle.github.io/Skater/ lucid https://github.com/tensorflow/lucid/ https://www.kdnuggets.com/2020/04/openai-open-sources-microscope-lucid-library-neural-networks.html what if tool https://pair-code.github.io/what-if-tool/ https://pair-code.github.io/what-if-tool/demos/uci.html themis https://themis-ml.readthedocs.io/en/latest/ DeepLIFT https://github.com/kundajelab/deeplift Arena https://medium.com/responsibleml/python-has-now-the-new-way-of-exploring-xai-explanations-4248846426cf tabnet https://cloud.google.com/blog/products/ai-machine-learning/ml-model-tabnet-is-easy-to-use-on-cloud-ai-platform explainerdashboard https://towardsdatascience.com/the-quickest-way-to-build-dashboards-for-machine-learning-models-ec769825070d Responsible AI-https://www.tensorflow.org/resources/responsible-ai fairlearn https://github.com/fairlearn/fairlearn fairml https://github.com/adebayoj/fairml https://www.datasciencecentral.com/profiles/blogs/fairml-auditing-black-box-predictive-models fair https://medium.com/responsibleml/how-to-easily-check-if-your-ml-model-is-fair-2c173419ae4c cleverhans https://github.com/cleverhans-lab/cleverhans Google Facets https://pair-code.github.io/facets/ Google’s Model Card Toolkit Opening the AI Black Box -https://zetane.com/gallery Rulex Explainable AI https://www.rulex.ai/rulex-explainable-ai-xai/ AI Explainability 360 Toolkit from IBM Research https://aix360.mybluemix.net/ https://analyticsindiamag.com/guide-to-ai-explainability-360-an-open-source-toolkit-by-ibm/ onnx https://github.com/onnx/onnx torch-dreams https://github.com/Mayukhdeb/torch-dreams https://github.com/jphall663/awesome-machine-learning-interpretability https://analyticsindiamag.com/8-explainable-ai-frameworks-driving-a-new-paradigm-for-transparency-in-ai/ https://christophm.github.io/interpretable-ml-book/ https://github.com/christophM/interpretable-ml-book https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html https://www.kdnuggets.com/2019/09/python-libraries-interpretable-machine-learning.html https://www.kdnuggets.com/2019/08/open-black-boxes-explainable-machine-learning.html Fairness https://analyticsindiamag.com/building-a-responsible-ai-eco-system/ How to easily check if your Machine Learning model is fair (dalex) https://www.kdnuggets.com/2020/12/machine-learning-model-fair.html TensorFlow Federated,TensorFlow Model Remediation,TensorFlow Privacy,LinkedIn Fairness Toolkit,Fairlearn,AI Fairness 360,Responsible AI Toolbox,XAI,scikit-fairness,Fairlead,Algofairness,Aequitas,CERTIFAI,ML-fairness-gym,Algofairness,FairSight,GD-IQ,scikit-fairness,Mitigating Gender Bias In Captioning System,Model Card Toolkit,AI Fairness 360, AI Explainability 360, Adversarial Robustness 360, Uncertainty Quantification 360, AI Privacy 360, Causal Inference 360, and AI FactSheets 360,Deon,Responsible AI Toolbox,DALEX,TensorFlow Data Validation,XAI,Fawkes,AdverTorch,solasai,Fawkes,Gluru,AdverTorch,Conversica,Quill AI,Fairness 360,Fairlead, TextAttack,Themis-ML,Debiaswe,fairness-in-ml,bias-correction,BlackBoxAuditing,fairness-indicators,Awesome-Fairness-in-AI https://analyticsindiamag.com/guide-to-ai-fairness-360-an-open-source-toolkit-for-detection-and-mitigation-of-bias-in-ml-models/ 107.deep-learning-drizzle -https://deep-learning-drizzle.github.io/ 108.Machine Learning University - https://aws.amazon.com/machine-learning/mlu/ 109.Continuous Machine Learning (CML),OpenMLOps,Metaflow,Kubeflow,Data Version Control (DVC),Kedro mlflow https://mlflow.org/ An open source platform for the machine learning lifecycle Layer https://docs.app.layer.ai/docs/ https://www.kdnuggets.com/2021/01/5-tools-effortless-data-science.html https://neptune.ai/ https://azure.microsoft.com/en-us/services/machine-learning/ https://github.com/VertaAI/modeldb 110.Data Preparation / ETL https://airflow.apache.org/ https://intake.readthedocs.io/en/latest/ 111.fairlearn https://github.com/fairlearn/fairlearn/blob/master/README.md Evaluating fairness of AI/ML models and training data and for mitigating bias in models determined to be unfair. AI Fairness 360 evaluating fairness of AI/ML models and training data and mitigating bias in current models https://aif360.mybluemix.net/ An ethics checklist for data scientists https://deon.drivendata.org/ 112.https://analyticsindiamag.com/top-6-ai-powered-drug-discovery-tools-in-2021/ MONAI Framework For Medical Imaging Research https://analyticsindiamag.com/monai-datatsets-managers/ torchio https://github.com/fepegar/torchio https://analyticsindiamag.com/torchio-3d-medical-imaging/ MolBert: Molecular Representation learning with AI medicalAI https://github.com/aibharata/medicalAI Biopython is a set of freely available tools https://github.com/biopython/biopython DeepIPW https://github.com/ruoqi-liu/DeepIPW 113.OpenVINO https://opencv.org/openvino-model-optimization/ https://opencv.org/how-to-speed-up-deep-learning-inference-using-openvino-toolkit-2/ 114.https://neptune.ai/blog/machine-learning-model-management https://analyticsindiamag.com/top-mlops-tools-github-repos/ https://neu.ro/2021-mlops-platforms-vendor-analysis-report/ Best Workflow and Pipeline Orchestration Tools https://neptune.ai/blog/best-workflow-and-pipeline-orchestration-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-workflow-and-pipeline-orchestration-tools MLflow vs Kubeflow vs Neptune https://neptune.ai/blog/mlflow-vs-kubeflow-vs-neptune-differences?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-mlflow-vs-kubeflow-vs-neptune-differences 15 MLOps.toys https://mlops.toys/ AIOps,Data version control DVC,MLFlow,Docker foundation,Kubernetes Foundation,Tensorflow Extend (TFX),Kubeflow,AWS AIOps,Azure AIOps,MLflow and TensorBoard ,Weights & Biases, Neptune AI, Comet,aim Data verification:Scale Nucleus,great_expectation,Soda Data Observability Metadata management:Neptune.ai,SiaSearch,Tensorflow's ML MetaData Data management:Neptune,DVC,RoboFlow,Dataiku,Apache Airflow, Apache NiFi, Apache Kafka Feature Stores : Amazon SageMaker Feature Store,Databricks,Hopsworks.ai,Vertex AI,FeatureForm,FeastTecton,butterfree,ByteHub Data Quality:whylogs,eurybia Detecting data drift and model drift:eurybia Experiment tracking :Kedro,modeldb,mlflow,DVC,weight and biases,Neptune,clearly,tensorboard,determined,polyaxon,mlrun,Comet,Sacred,TensorBoard,DagsHub,Guild AI,ClearML,Valohai,Pachyderm,Verta.ai,Kubeflow,SageMaker Studio,sacred Monitoring: Prometheus, Grafana, ELK Stack Data versioning:Dolt,DVC,gitlfs,pachyderm, Git LFS,lakefs,DVC,weight and biases,Neptune,Comet,Delta Lake Data Governance: Collibra, Alation, Informatica Data Quality: Trifacta, Talend, Informatica Code versioning: Gitlab,github,SVN Model Versioning :Neptune,ModelDB,DVC,MLFlow,Pachyderm,Polyaxon Pipeline orchestration:Kale,Apche airflow,Argo,workflows,Luigi,kubeflow,kedro,nextflow,dragster,Apache,bean,zenml,flute,prefect,ray,DVC,polyaxon,clearml,mlrun,pachyderm,Metaflow,Couler,Valohai,Dagster.io Runtime engine:Ray,nuclio,dask,horovod,Apache,spark Data orchestration prefect,kale,mlru,dagster,kedro,airflow Artifact tracking:Kubeflow,mlflow,weight and biases,Neptune,polyaxon,clearml,mlrun,pachyderm Model registry:Modeldb,mlflow,determined,weight and biases,Neptune,clearml,mlrun, Vision AI,DINO,Amazon Rekognition Model serving:Seldon,core,bentoml,tensorflow serving,kserve,fastapi,torchserve,ray,mlflow,clearml,mlrun,pymlpipe,TorchServe,TensorFlow Serving,Kubeflow,Cortex,Seldon.ai,ForestFlow,bentoml Model monitoring:Evidently,WhyLabs,grafana,alibi,detect,modeldb,clearml,mlrun,prometheus,pymlpipe,NannyML,Aporia,eurybia,Arize,Fiddler,Amazon SageMaker Model Monitor,Prometheus,Qualdo,Neptune,Grafana + Prometheus ,Qualdo,Seldon Core,Censius Model Performance Tracking: TensorBoard, MLflow, Comet.ml Continuous Integration: Jenkins, Travis CI, CircleCI Continuous Deployment: Jenkins, Travis CI, CircleCI Containerization: Docker, Kubernetes Configuration Management: Ansible, Puppet, Chef data validation:Pydantic,eurybia model testing: Deepchecks,Neptune,Mona ,Grafana + Prometheus Model Security: Seldon, OpenVino, TensorFlow Privacy Continuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning : CML ,GitHub Actions,GitLab for CI/CD,Jenkins,TeamCity,Circle CI,Travis CI, aim https://github.com/aimhubio/aim Metaflow,MLReef,MLRun,ZenML,MLflow,Seldon,Bodywork,Pachyderm,DVC, or Data Version Control MLOps https://analyticsindiamag.com/8-projects-to-kickstart-your-mlops-journey-in-2021/ Open MLOps https://github.com/datarevenue-berlin/OpenMLOps Best Tools for Tracking Machine Learning Experiments https://neptune.ai/blog/best-ml-experiment-tracking-tools mlops-https://github.com/visenger/awesome-mlops mlflow https://towardsdatascience.com/get-started-with-mlops-fd7062cab018 GuildAI https://guild.ai/ https://github.com/guildai/guildai MLOPS https://www.analyticsinsight.net/top-mlops-based-tools-for-enabling-effective-machine-learning-lifecycle/ https://neptune.ai/blog/best-mlops-tools ML-Model-CI https://github.com/cap-ntu/ML-Model-CI Easy MLOps with PyCaret + MLflow https://www.kdnuggets.com/2021/05/easy-mlops-pycaret-mlflow.html https://www.kdnuggets.com/2021/03/overview-mlops.html https://medium.com/prosus-ai-tech-blog/towards-mlops-technical-capabilities-of-a-machine-learning-platform-61f504e3e281 omegaml https://github.com/omegaml/omegaml https://neptune.ai/blog/8-best-data-science-and-machine-learning-platforms-for-mlops?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-8-best-data-science-and-machine-learning-platforms-for-mlops https://neptune.ai/blog/ml-model-monitoring-best-tools?utm_source=email&utm_medium=newsletter&utm_campaign=blog-march&utm_content=ml-model-monitoring-best-tools https://neptune.ai/blog/end-to-end-mlops-platforms?utm_source=email&utm_medium=newsletter&utm_campaign=blog-march&utm_content=end-to-end-mlops-platforms https://neptune.ai/blog/mlops-at-greensteam-shipping-machine-learning-case-study?utm_source=email&utm_medium=newsletter&utm_campaign=blog-march&utm_content=mlops-at-greensteam-shipping-machine-learning-case-study https://neptune.ai/blog/mlops-10-best-practices?utm_source=email&utm_medium=newsletter&utm_campaign=blog-march&utm_content=mlops-10-best-practices https://neptune.ai/blog/machine-learning-model-management?utm_source=email&utm_medium=newsletter&utm_campaign=blog-march&utm_content=machine-learning-model-management https://mlops.githubapp.com/ https://about.mlreef.com/blog/global-mlops-and-ml-tools-landscape https://github.com/paiml/practical-mlops-book https://olympus.greatlearning.in/courses/12956?_gl=1*ljadx1*_ga*NjMxNjUxNjM2LjE2MDYyMDYzNDM.*_ga_TH52C020P8*MTYxMTIyOTQ0MS40Ny4wLjE2MTEyMjk0NDEuNjA. https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-technical-paper https://neptune.ai/blog/end-to-end-mlops-platforms https://github.com/kelvins/awesome-mlops#hyperparameter-tuning ClearML https://analyticsindiamag.com/guide-to-clearml-zero-integration-mlops-solution/ https://neptune.ai/blog/mlops-what-it-is-why-it-matters-and-how-to-implement-it-from-a-data-scientist-perspective?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-mlops-what-it-is-why-it-matters-and-how-to-implement-it-from-a-data-scientist-perspective https://ml-ops.org/content/mlops-principles Monitoring: Evidently https://evidentlyai.com/ , Seldon Alibi https://github.com/SeldonIO/alibi-detect 115.Code faster https://www.tabnine.com/ 117.https://www.pye.ai/2021/03/19/machine-learning-model-management-what-why-and-how/ https://www.ambiata.com/blog/2020-12-07-mlops-tools/ Pachyderm Kubeflow MLflow Metaflow ZenML Seldon Bodywork MLReef MLRun DVC katana-skipper Weights & Biases Valohai Polyaxon Neptune.ai CometML Algorithmia clearml, airflow, kedro, GitHub Actions Flyte Valohai Seldon Iguazio Datarobot Dataiku cnvrg.io ClearML AWS Sagemaker wandb evidently BentoML Unified Model Serving Framework https://github.com/bentoml/BentoML mlflow https://mlflow.org/docs/latest/index.html https://github.com/amesar/mlflow-examples MLFlow by pycaret https://pycaret.org/mlflow/?utm_medium=social&utm_source=linkedin&utm_campaign=postfity&utm_content=postfity2c1c2 labml https://ramith.fyi/tracking-your-ml-experiments-without-sending-data-to-the-cloud/ MLOps https://github.com/microsoft/MLOps https://mlops.githubapp.com/ https://huyenchip.com/2020/12/30/mlops-v2.html https://github.com/paiml/practical-mlops-book https://analyticsindiamag.com/top-10-tools-to-kickstart-your-mlops-journey-in-2021 mlops platform SageMaker on Amazon,Data Lab,Domino,H2O MLOps,Cloudera,Data Platform,Kubeflow,MLFlow,Metaflow,Flyte,ZenML,MLRun,Algorithmia,Dataiku,DataRobot,Pachyderm,Databricks,Lakehouse,Neptune.ai 7 Best Resources To Learn MLOps In 2021 https://analyticsindiamag.com/7-best-resources-to-learn-mlops-in-2021/ DevOps https://github.com/collections/devops-tools airflow https://github.com/apache/airflow kubeflow https://github.com/kubeflow/kubeflow kubernetes https://github.com/kubernetes/kubernetes Metaflow https://metaflow.org/ https://github.com/Netflix/metaflow pipeline https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html Tensorflow Extended https://www.tensorflow.org/tfx Tensorflow Transform https://www.tensorflow.org/tfx/transform/get_started https://aniruddha-choudhury49.medium.com/mlops-kubeflow-with-tensorflow-tfx-pipelines-seamlessly-and-at-scale-92b432bd39b0 Serving Models https://www.tensorflow.org/tfx/guide/serving Tensorflow Data Validation https://www.tensorflow.org/tfx/data_validation/get_started TensorFlow Model Analysis https://www.tensorflow.org/tfx/model_analysis/get_started Model Validation Toolkit https://finraos.github.io/model-validation-toolkit/ https://github.com/FINRAOS/model-validation-toolkit MLflow Open-source platform for tracking machine learning experiments https://mlflow.org/ https://analyticsindiamag.com/guide-to-mlflow-a-platform-to-manage-machine-learning-lifecycle/ https://www.kdnuggets.com/2021/01/model-experiments-tracking-registration-mlflow-databricks.html ray https://docs.ray.io/en/master/serve/ https://github.com/ray-project/ray https://medium.com/distributed-computing-with-ray/ray-mlflow-taking-distributed-machine-learning-applications-to-production-103f5505cb88 118. Feature Stores https://neptune.ai/blog/feature-stores-components-of-a-data-science-factory-guide?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-feature-stores-components-of-a-data-science-factory-guide Top 10 Leading Machine Learning Feature Stores https://www.pye.ai/2021/05/14/top-10-machine-learning-feature-store-systems/ 118.algorithm to use by problem https://www.datasciencecentral.com/profiles/blogs/which-machine-learning-deep-learning-algorithm-to-use-by-problem 119.Connect the world to your data and fuel your ML. OpenBlender Enrich ML Models with adding new Variables from Any Source to Boost Performance https://www.youtube.com/channel/UCCFN8DDrA6k7eHYLvZGdNVA https://openblender.io/ 120. Google's MuRIL (Multilingual Representations for Indian Languages) https://tfhub.dev/google/MuRIL/1 121.mxnet https://mxnet.apache.org/versions/master/api/python/docs/tutorials/getting-started/crash-course/index.html 122.tools-https://towardsdatascience.com/data-science-tools-f16ecd91c95d 123.Elements of AI free online course https://www.elementsofai.com/ 124.Best_AI_paper_2020 https://github.com/louisfb01/Best_AI_paper_2020 125.roadmap https://github.com/graykode/nlp-roadmap https://www.theinsaneapp.com/2021/03/roadmap-series.html https://www.freecodecamp.org/news/data-science-learning-roadmap/ https://www.kdnuggets.com/2020/12/roadmaps-ai-developer-data-scientist-machine-learning-engineer.html https://mohammedazeem665.medium.com/plan-to-learn-machine-learning-data-science-in-2021-note-these-assets-from-2020-e84389d94097 https://github.com/AMAI-GmbH/AI-Expert-Roadmap https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463 data-engineer-roadmap https://github.com/datastacktv/data-engineer-roadmap 126.https://neptune.ai/blog/best-data-science-tools-to-increase-machine-learning-model-understanding?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-data-science-tools-to-increase-machine-learning-model-understanding Visualizing the Execution of Python Program http://pythontutor.com/ https://www.youtube.com/watch?v=pCSlWQjfCzA MLPerf Model performance debugging tools https://mlperf.org/ Model debugging tools Manifold https://eng.uber.com/manifold/ Pytest for Data Scientists https://towardsdatascience.com/4-lessor-known-yet-awesome-tips-for-pytest-2117d8a62d9c Icecream https://towardsdatascience.com/stop-using-print-to-debug-in-python-use-icecream-instead-79e17b963fcc Experiment tracking tools WandB https://wandb.ai/site Comet manage and organize machine learning experiments https://www.comet.ml/site/ https://analyticsindiamag.com/how-to-supercharge-your-machine-learning-experiments-with-comet-ml/ neptune https://neptune.ai/ https://analyticsindiamag.com/how-to-manage-ml-experiments-with-neptune-ai/ weights & biases https://wandb.ai/site https://analyticsindiamag.com/hands-on-guide-to-weights-and-biases-wandb-with-python-implementation/ https://docs.wandb.ai/ https://www.kdnuggets.com/2020/07/tour-end-to-end-machine-learning-platforms.html 127.19 Best JupyterLab Extensions for Machine Learning https://neptune.ai/blog/jupyterlab-extensions-for-machine-learning 128.coreml https://developer.apple.com/machine-learning/core-ml/ 129.Protect Your Neural Networks Against Hacking Adversarial Robustness Toolbox (ART) https://analyticsindiamag.com/adversarial-robustness-toolbox-art/ 130.https://www.kdnuggets.com/2021/01/10-underappreciated-python-packages-machine-learning-practitioners.html 131.datascience-fails https://github.com/xLaszlo/datascience-fails 132.Jupyter notebook integration for Microsoft Excel https://github.com/pyxll/pyxll-jupyter https://towardsdatascience.com/python-jupyter-notebooks-in-excel-5ab34fc6439 Voilà turns Jupyter notebooks into standalone web applications https://github.com/voila-dashboards/voila https://github.com/voila-dashboards/voila-gridstack How to Optimize Your Jupyter Notebook https://www.kdnuggets.com/2020/01/optimize-jupyter-notebook.html TabNet: Attentive Interpretable Tabular Learning https://github.com/dreamquark-ai/tabnet 133.rapidly develop data applications with Python https://github.com/dstackai/dstack 134.Google Research: Looking Back at 2020, and Forward to 2021 https://ai.googleblog.com/2021/01/google-research-looking-back-at-2020.html 135.cortex Run inference at scale https://www.cortex.dev/ https://github.com/cortexlabs/cortex 136.https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html 137.Federated Learning Systems Flower – A Framework To Build Federated Learning Systems https://github.com/adap/flower https://flower.dev/ 138.https://analyticsindiamag.com/top-ai-powered-writing-assistants-to-create-better-content/ 139.Tensorflow Data Validation - Data Analysis At Scale https://www.youtube.com/watch?v=eGIG_qHgQ08 140.SciKeras https://scikeras.readthedocs.io/en/latest/# 141.debugging Data viewer https://devblogs.microsoft.com/python/python-in-visual-studio-code-january-2021-release/ 142.Machine Learning Lifecycle in 2021 https://towardsdatascience.com/the-machine-learning-lifecycle-in-2021-473717c633bc 143.Introduction To ML.NET – An ML Framework For DOTNET Developers https://analyticsindiamag.com/introduction-to-ml-net-a-machine-learning-framework-for-dotnet-developers/ https://analyticsindiamag.com/step-by-step-guide-for-image-classification-using-ml-net/ 144.https://www.perceptilabs.com/home http://deeplearninggallery.com/ https://www.kdnuggets.com/2019/01/practical-apache-spark-10-minutes.html 145.https://www.kdnuggets.com/2018/09/machine-learning-cheat-sheets.html https://www.kdnuggets.com/2018/09/meverick-lin-data-science-cheat-sheet.html https://www.kdnuggets.com/2018/08/data-visualization-cheatsheet.html https://www.kdnuggets.com/2018/07/sql-cheat-sheet.html https://www.kdnuggets.com/2018/04/python-regular-expressions-cheat-sheet.html https://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html https://www.analyticsvidhya.com/blog/2021/01/5-python-packages-every-data-scientist-must-know/ https://www.kdnuggets.com/2021/01/ultimate-scikit-learn-machine-learning-cheatsheet.html https://www.kdnuggets.com/2020/09/10-things-know-scikit-learn.html 146.Data Pipelines https://www.kdnuggets.com/2018/05/beginners-guide-data-science-pipeline.html https://www.kdnuggets.com/2019/03/data-pipelines-luigi-airflow-everything-need-know.html 147. AI Habitat: A Platform For Embodied AI Research https://analyticsindiamag.com/hands-on-guide-to-ai-habitat-a-platform-for-embodied-ai-research/ 149.onnx https://medium.com/towards-artificial-intelligence/onnx-for-model-interoperability-faster-inference-8709375db9bf 152.Best ML Frameworks & Extensions for Scikit-learn https://neptune.ai/blog/the-best-ml-framework-extensions-for-scikit-learn?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-the-best-ml-framework-extensions-for-scikit-learn 153.Multimodal Neurons, The Most Advanced Neural Networks Discovered By OpenAI https://analyticsindiamag.com/inside-multimodal-neurons-the-most-advanced-neural-networks-discovered-by-openai/ 154.TensorGram https://github.com/ksdkamesh99/TensorGram https://www.youtube.com/watch?v=ItDBQB4YFuI knockknock https://towardsdatascience.com/how-to-get-notified-when-your-model-is-done-training-with-knockknock-483a0475f82c labmi Organize machine learning experiments and monitor training progress from mobile https://labml.ai/ WeightWatcher https://github.com/CalculatedContent/WeightWatcher labml Monitor deep learning model training and hardware usage from your mobile phone https://labml.ai/ https://github.com/labmlai/labml ml notify https://github.com/aporia-ai/mlnotify 155.r packages https://upurl.me/vkf3r http://r-bloggers.com/2021/04/15-essential-packages-in-r-for-data-science/ https://www.ubuntupit.com/best-r-machine-learning-packages/ Top 10 Free Resources To Learn R https://analyticsindiamag.com/top-10-free-resources-to-learn-r/ https://bluemind1988.medium.com/explore-r-libraries-for-end-to-end-data-science-projects-b4d0af3a9f5c analyticsvidhya.com/blog/2021/04/top-10-r-packages-for-data-science-you-must-know-in-2021/ 156.Top Julia Libraries for Machine Learning https://www.analyticsvidhya.com/blog/2021/05/top-julia-machine-learning-libraries/ 156.openblender Fuel your ML Engines with Relevant Data to Boost Performance https://openblender.io/#/welcome 157.all Domain-based A.I. Platform for Data Scientists https://www.cluzters.ai/ 158.2D images to 3D https://analyticsindiamag.com/python-guide-to-neural-body-converting-2d-images-to-3d/ Open3D: An Open Source Modern Library For 3D Data Processing https://github.com/intel-isl/Open3D 160.https://gallery.allennlp.org/ https://prior.allenai.org/projects/gpv 161.NVIDIA Unveils 50+ New, Updated AI Tools and Trainings for Developers https://www.hpcwire.com/off-the-wire/nvidia-unveils-50-new-updated-ai-tools-and-trainings-for-developers/ 162.Best Workflow and Pipeline Orchestration Tools https://neptune.ai/blog/best-workflow-and-pipeline-orchestration-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-workflow-and-pipeline-orchestration-tools 164.notes Data Science & Machine Learning https://chrisalbon.com/ 165.black uncompromising Python code formatter https://github.com/psf/black 166.Feature stores https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html https://neptune.ai/blog/feature-stores-components-of-a-data-science-factory-guide?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-feature-stores-components-of-a-data-science-factory-guide 167.Code and Notebook Versioning for ML Teams https://neptune.ai/blog/code-and-notebook-versioning-for-ml-teams-guide?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-code-and-notebook-versioning-for-ml-teams-guide 10 tools that can serve as a great alternative to the different parts of ClearML https://neptune.ai/blog/clear-ml-alternatives?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-clear-ml-alternatives 168.3 Tools to Track and Visualize the Execution of your Python Code https://towardsdatascience.com/3-tools-to-track-and-visualize-the-execution-of-your-python-code-666a153e435e ***Follow leaders in the field to update yourself in the field*** 1.Linkedin 2.Twitter ***CPU/GPU/TPU*** 1.Google cloab (FREE) Jupyter Lab for Python, R, Swift from Google Colab with ColabCode https://www.youtube.com/watch?v=Q35WIqZoUF4 https://www.analyticsvidhya.com/blog/2021/01/avid-user-of-google-colab-here-are-some-alternatives-of-google-colab-that-you-should-know-about/?utm_source=linkedin&utm_medium=social&utm_campaign=old-blog&utm_content=B&custom=FBI156 https://towardsdatascience.com/use-colab-more-efficiently-with-these-hacks-fc89ef1162d8 https://www.analyticsvidhya.com/blog/2021/05/10-colab-tips-and-hacks-for-efficient-use-of-it/ ColabCode This is an amazing extension to the already available resource, Google Colab https://github.com/abhi1thakur/colabcode GitHub notebooks with Google Colab https://www.youtube.com/watch?v=LmIylxNmA-A&feature=youtu.be colab_everything Python library to run streamlit, flask, fastapi, etc on google colab https://github.com/Ankur-singh/colab_everything/ 2.Kaggle kernel(read terms and conditions before use) (FREE) 3.Paperspace Gradient(read terms and conditions before use) 4.knime - https://www.knime.com/(read terms and conditions before use) 5.RapidMiner (read terms and conditions before use) https://github.com/zszazi/Deep-learning-in-cloud 6.saturncloud https://saturncloud.io/ Intel Jupyter Lab,Amazon Sagemaker,Binder,DeepNote,Hex,DataBricks Notebook,Jetbrains Datalore,DataCamp Workspace,Notablejournal,Notable,Observable,CoCalc,Replit,Binder,IBM DataPlatform Notebooks,CodeSandbox,StackBlitz ***So what next ?*** participate online competition and do project and apply to intership ,job,solving real world problems, etc... applications of data science in many industry 1.E-commerce- Identifying consumers,Recommending Products,Analyzing Reviews 2.Manufacturing- Predicting potential problems,Monitoring systems,Automating manufacturing units, Maintenance Scheduling,Anomaly Detection 3.Banking- Fraud detection,Credit risk modeling,Customer lifetime value 4.Healthcare- Medical image analysis, Drug discovery,Bioinformatics,Virtual Assistants,image segmentation 5.Transport- Self-driving cars,Enhanced driving experience,Car monitoring system,Enhancing the safety of passengers 6.Finance- Customer segmentation,Strategic decision making,Algorithmic trading,Risk analytics 7.Marketing (Added from comments Credits: Jawad Ali)- LTV predictions,Predictive analytics for customer behavior,Ad targeting and many more fields - https://www.topbots.com/enterprise-ai-companies-2020/ , https://venturebeat.com/2020/10/21/the-2020-data-and-ai-landscape/ ***Research blogs*** https://www.theinsaneapp.com/2021/04/top-machine-learning-blogs-to-follow-in-2021.html Explainpaper https://www.explainpaper.com/ https://reconshell.com/top-ai-and-machine-learning-blogs-curated-for-ai-enthusiasts/ 1.https://ai.facebook.com/ https://ai.facebook.com/blog/ 2.https://ai.googleblog.com/ 3.https://deepmind.com/blog https://deepai.org/definitions 4.https://openai.com/blog/ 5.https://www.malongtech.com/en/research.html 6.https://blogs.nvidia.com/blog/tag/artificial-intelligence/ https://blogs.nvidia.com/ https://ai.googleblog.com/2021/01/google-research-looking-back-at-2020.html?m=1 7.https://blog.tensorflow.org/ 8.https://pytorch.org/blog/ 9.https://distill.pub/ kdnuggets.com https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-know.html ***RESEARCH LABS IN THE WORLD*** https://ai.facebook.com/ https://ai.googleblog.com/ https://research.google/ https://ai.google/research/ 1.The Alan Turing Institute:https://www.turing.ac.uk/ 2.J.P. Morgan AI Research Lab:https://www.jpmorgan.com/insights/tec... 3.Oxford ML Research Group:http://www.robots.ox.ac.uk/~parg/proj... 4.Microsoft Research Lab- AI:https://www.microsoft.com/en-us/resea... 5.Berkeley AI Research:https://bair.berkeley.edu/ 6.LIVIA:https://en.etsmtl.ca/Unites-de-recher... 7.MIT Computer Science and Artificial :https://www.csail.mit.edu/ ***online competitions:*** Top 25 Machine Learning Hackathons https://medium.com/analytics-vidhya/top-25-machine-learning-hackathons-its-here-now-for-anyone-to-move-to-data-science-a93deb2a198a 1.Kaggle-https://www.kaggle.com/ kaggle-solutions https://github.com/faridrashidi/kaggle-solutions 2.hackerearth-https://www.hackerearth.com/challenges/ 3.machinehack-https://www.machinehack.com/ 4.analyticsvidhya-https://datahack.analyticsvidhya.com/contest/all/ 5.zindi-https://zindi.africa/competitions 6.crowdai-https://www.crowdai.org/ 7.driven data-https://www.drivendata.org/ 8.dockship-https://dockship.io/Runway AI 9.SIGNATE Competition- https://signate.jp/about?rf=competition_about 9.International Data Analysis Olympiad (IDAHO) 10.Codalab 11.Iron Viz 12.Data Science Challenges 13.Tianchi Big Data Competition 14.https://www.techgig.com/hackathon/ml_hackathon 15.https://www.openml.org/ https://towardsdatascience.com/12-data-science-ai-competitions-to-advance-your-skills-in-2021-32e3fcb95d8c https://www.kdnuggets.com/2020/09/international-alternatives-kaggle-data-science-competitions.html ***Some useful content :*** 1. H20.ai automl, google automl,Google Cloud AutoML,google ml kit(https://developers.google.com/ml-kit) ,Azure Cognitive Services,Azure Machine Learning Service,amazon ml,Azure Machine Learning Studio,Google Cloud Platform,gcp automl ision,Weka,AutoWeka,Microsoft Cognitive Toolkit,Google Cloud AutoML,DataRobot AutoML,Databricks AutoML,Azure ML,azure machine learning studio,IBM Watson ml studio,AWS Sagemaker Studio,aws rekognition,Google AI Platform,Databricks,Domino Data Lab,roboflow,Qlik AutoML,NVIDIA TAO H2O Driverless AI https://www.h2o.ai/products/h2o-driverless-ai/ H2O Flow - Web Based Machine Learning Development https://docs.h2o.ai/h2o/latest-stable/h2o-docs/flow.html https://www.analyticsvidhya.com/blog/2021/05/a-step-by-step-guide-to-automl-with-h2o-flow/ https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet https://neptune.ai/blog/best-machine-learning-as-a-service-platforms-mlaas?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-machine-learning-as-a-service-platforms-mlaas https://codegnan.com/blog/35-best-data-sciecne-tools-for-beginners-to-master/ https://analyticsindiamag.com/free-online-resources-to-learn-automl/ https://analyticsindiamag.com/10-popular-automl-tools-developers-can-use/ https://analyticsindiamag.com/8-best-open-source-tools-for-data-mining/ mlkit-https://firebase.google.com/products/ml runway https://runwayml.com/ fritz https://www.fritz.ai/ obviously https://www.obviously.ai/ createml https://developer.apple.com/machine-learning/create-ml/ makeml https://makeml.app/ superannotate https://superannotate.com/ https://rapidminer.com/ https://monkeylearn.com/monkeylearn-studio/ https://nanonets.com/ GCP Professional ML Engineer certification in 8 days https://ml-rafiqhasan.medium.com/how-i-cracked-the-gcp-professional-ml-engineer-certification-in-8-days-f341cf0bc5a0 Vertex AI, one platform, every ML tool you need https://cloud.google.com/vertex-ai 2.FasterAI,keras,fastai,tesorflow,pytorch Automated model architecture search tools (e.g. darts, enas) https://awesomeopensource.com/projects/automl https://github.com/search?q=automl https://www.kdnuggets.com/2016/03/automated-data-science.html https://www.kdnuggets.com/software/automated-data-science.html Tpot https://github.com/EpistasisLab/tpot ATOM https://github.com/tvdboom/ATOM https://towardsdatascience.com/how-to-test-multiple-machine-learning-pipelines-with-just-a-few-lines-of-python-1a16cb4686d mljar-supervised https://github.com/mljar/mljar-supervised libra end-to-end machine learning process in just one line of code https://github.com/Palashio/libra featurewiz, boruta_py ,AutoWeka,Auto-Sklearn,AutoGluon,Auto-PyTorch,AutoKeras,auto-tensorflow,Ludwig,MLBox,PyCaret,LightAutoML,FLAML,EvalML,H2O AutoML GML https://github.com/Muhammad4hmed/GML auto_ml https://github.com/ClimbsRocks/auto_ml automl-gs Automating Machine Learning In A Single Line Of Code https://github.com/minimaxir/automl-gs paddlehub Performing Computer Vision & NLP Tasks in a Single Of Code https://github.com/PaddlePaddle/PaddleHub pywedge https://github.com/taknev83/pywedge https://towardsdatascience.com/automated-interactive-package-for-eda-modeling-and-hyperparameter-tuning-in-a-few-lines-of-228c561fa63c LightAutoML https://github.com/sberbank-ai-lab/LightAutoML https://lightautoml.readthedocs.io/en/latest/ https://towardsdatascience.com/lightautoml-preset-usage-tutorial-2cce7da6f936 FLAML fast and lightweight AutoML library https://github.com/microsoft/FLAML LightAutoML LAMA - automatic model creation framework https://github.com/sberbank-ai-lab/LightAutoML H2O Hydrogen Torch: A No-code Deep Learning Framework EvalML is an AutoML library https://github.com/alteryx/evalml https://evalml.alteryx.com/en/stable/ https://www.kdnuggets.com/2021/04/easy-automl-python.html https://www.youtube.com/watch?v=uuYEQqrExBQ https://www.analyticsvidhya.com/blog/2021/05/machine-learning-automation-using-evalml-library/ dataprep Beginners Guide to Automation in Data Science https://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-automation-in-data-science/ A machine learning tool for automated prediction engineering https://github.com/alteryx/compose adanet https://github.com/tensorflow/adanet mljar-supervised https://github.com/mljar/mljar-supervised/ https://www.kdnuggets.com/2021/05/binary-classification-automated-machine-learning.html ludwig https://github.com/ludwig-ai/ludwig carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch https://carefree0910.me/carefree-learn-doc/ autoweka https://github.com/automl/autoweka ATOM Automated Tool for Optimized Modelling https://github.com/tvdboom/ATOM autokeras https://autokeras.com/ autoSklearn https://automl.github.io/auto-sklearn/master/ baytune auto-tuning https://github.com/MLBazaar/BTB storm-tuner Best Hyper Parameters For Deep Learning Model https://github.com/ben-arnao/StoRM adanet https://github.com/tensorflow/adanet AlphaPy Automated Machine Learning https://github.com/ScottfreeLLC/AlphaPy TransmogrifAI https://github.com/salesforce/TransmogrifAI Hugging Face’s AutoNLP https://www.analyticsvidhya.com/blog/2021/03/a-hands-on-introduction-to-hugging-faces-autonlp-101/ complex Machine Learning model in one line with Libra https://github.com/Palashio/libra Automated Text Classification with EvalML https://www.kdnuggets.com/2021/04/automated-text-classification-evalml.html Pywedge A complete package for EDA, Data Preprocessing and Modelling https://towardsdatascience.com/pywedge-a-complete-package-for-eda-data-preprocessing-and-modelling-32171702a1e0 3.awesome-AutoML https://github.com/windmaple/awesome-AutoML , automl-gs github.com/minimaxir/automl-gs autopandas,Auto-Sklearn,Auto-Pytorch,Auto-ViML,AutoViz,AutoGluon,MLBox,FLAML,EvalML,scikit-optimize,Hyperopt-Sklearn,smac3,alphapy,nni,adanet,ludwig, TPOT,flaml, H2OAutoML ,automl ,LightAutoML,auto keras,MLJAR,PyCaret,Auto-sklearn,SMAC,WALTS Auto-PyTorch,Keras Tuner,DataRobot, DriverlessAI , MLBox, AutoGluon, autoweka, Amazon Lex,Darwin,AdaNet, Microsoft NNI,GradsFlow,Ludwig,autoai,Get Duet,Qlik AutoML,NeutonAutoML,Clarifai,CreateML,Lobe,ObviouslyAI,RunwayML,neuton automl,TransmogrifAI,Rapid Miner,Dataiku,DataRobot,H2O Driverless,Amazon Lex, BigML,AutoML JADBio,Akkio MLJAR, Tazi.ai,UBER’s Ludwig,ANAI,Google Vizier,Tune,HpBandSter,Hyperopt,Facebook’s HiPlot,Bayesian Optimisation,SmartML,SigOpt,Talos,mlmachine,SHERPA Scikit-Optimize,Microsoft’s NNI,Google’s Vizer,GPyOpt,Hyperopt Metric Optimisation Engine (MOE),Optuna,Ray Tune,Keras Tuner,TransmogrifAI Automated Tensorflow https://github.com/rafiqhasan/auto-tensorflow MLBox https://github.com/AxeldeRomblay/MLBox skycube automl https://skycube.app/ stackml Machine Learning platform in the browser https://stackml.com/ quick_ml https://pypi.org/project/quick-ml/ https://www.quickml.info/ MLJAR https://github.com/mljar/mljar-supervised/ https://towardsdatascience.com/binary-classification-with-automated-machine-learning-1a36e78ba50f TransmogrifAI https://github.com/salesforce/TransmogrifAI darwin http://drwn.anu.edu.au/ GenoML (AutoML) for Genomics https://genoml.com/ https://github.com/GenoML baytune https://www.kdnuggets.com/2021/03/automating-machine-learning-model-optimization.html https://github.com/MLBazaar/BTB adanet https://github.com/tensorflow/adanet FEDOT Automated modeling and machine learning framework FEDOT https://github.com/nccr-itmo/FEDOT 4.AutoGluon AutoML for Text, Image, and Tabular Data https://analyticsindiamag.com/how-to-automate-machine-learning-tasks-using-autogluon/ AutoGL: The First Ever AutoML Framework for Graph Datasets https://analyticsindiamag.com/meet-autogl-the-first-ever-automl-framework-for-graph-datasets/ Neuton TinyML https://neuton.ai/ 5. auto sklearn,auto keras,auto Tensorflow,autoLightAutoML,xcessiv,kerastuner ,LAMA, NNI, FEDOT (https://github.com/sberbank-ai-lab/LightAutoML) deephyper Automating Deep Neural Networks https://github.com/deephyper/deephyper Keras Tuner or storm-tuner - Decide Number of Hidden Layers And Neuron In Neural Network AutoNeuro https://autoneuro.challenge-ineuron.in/ ATOM https://towardsdatascience.com/atom-a-python-package-for-fast-exploration-of-machine-learning-pipelines-653956a16e7b https://github.com/tvdboom/ATOM 6. autoviml https://github.com/AutoViML/Auto_ViML https://towardsdatascience.com/autoviml-automating-machine-learning-4792fee6ae1e deep_autoviml https://github.com/AutoViML/deep_autoviml 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 https://github.com/Muhammad4hmed/GML CodeLess https://pypi.org/project/codeless/ https://github.com/porky5191/codeless_demo_project AUTORL: AUTOML FOR RL https://www.automl.org/blog-autorl/ 8. sweetviz (EDA purpose) - https://pypi.org/project/sweetviz/ https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html 9. pandasprofiling(display whole EDA) - https://pypi.org/project/pandas-profiling/ https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/index.html 10. autokeras,AutoSklearn,Neural Network Intelligence FeatureTools automated feature engineering. MLBox,Lightwood,mindsdb(machine learning models using SQL queries),mljar-supervised,Ludwig(deep learning models without the need to write code) AdaNet is a lightweight TensorFlow-based framework 11. pycaret- https://pycaret.org/ https://www.kdnuggets.com/2020/08/build-automl-pycaret.html https://www.kdnuggets.com/2020/08/github-best-automl-ever-need.html https://www.kdnuggets.com/2020/07/5-things-pycaret.html Machine Learning in Power BI using PyCaret https://www.kdnuggets.com/2020/05/machine-learning-power-bi-pycaret.html https://towardsdatascience.com/build-your-first-anomaly-detector-in-power-bi-using-pycaret-2b41b363244e https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html https://www.kdnuggets.com/2020/08/github-best-automl-ever-need.html mindsdb Machine Learning in 5 Lines of Code https://mindsdb.com/ automated feature engineering https://github.com/alteryx/featuretools https://towardsdatascience.com/why-automated-feature-engineering-will-change-the-way-you-do-machine-learning-5c15bf188b96 Featuretools https://www.featuretools.com/ Automate your ML Pipelines with EvalML https://analyticsindiamag.com/automate-your-ml-pipelines-with-evalml/ Aethos — A Data Science Library to Automate your Workflow https://towardsdatascience.com/aethos-a-data-science-library-to-automate-workflow-17cd76b073a4 AutoAI — Automating the AI Workflow to Build & Deploy Machine Learning model https://medium.com/geekculture/autoai-automating-the-ai-workflow-to-build-deploy-machine-learning-model-bb2b727cda28 AutoML toolkit https://github.com/microsoft/nni LightAutoML LAMA - automatic model creation framework https://github.com/sberbank-ai-lab/LightAutoML https://analyticsindiamag.com/hands-on-python-guide-to-lama-an-automatic-ml-model-creation-framework/ LightAutoML https://github.com/sb-ai-lab/LightAutoML mljar-supervised Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning https://github.com/mljar/mljar-supervised MLBox is a powerful Automated Machine Learning python library https://github.com/AxeldeRomblay/MLBox 12.Auto_Timeseries by auto_ts 13.AutoNLP_Sentiment_Analysis by autoviml 14.automl lazypredict https://github.com/shankarpandala/lazypredict AutoML Toolkit for Graph Datasets & Tasks AutoGL(Auto Graph Learning)https://medium.com/syncedreview/tsinghua-university-releases-first-automl-toolkit-for-graph-datasets-tasks-c61ea0261d78 AutoFeat-https://analyticsindiamag.com/guide-to-automatic-feature-engineering-using-autofeat/ 15.https://github.com/mstaniak/autoEDA-resources mito , dtale bamboolib or pandas-ui or pandas-summary or pandas_visual_analysis or Dtale(get code also) (python package for easy data exploration & transformation) Automating EDA using Pandas Profiling, streamlit_pandas_profiling,Sweetviz and Autoviz,DataPrep,vaex,Datapane,Sweetviz,pandas_UI,PandasGUI,Datatable,Dora,Pywedge,D-Tale,lux,Dabl,Pretty pandas,data_describe,Sparkora,AWS Glue DataBrew,speedML,edaviz,Altair,voyager,Mito,Facets,KNIME,lux,datatable,Pandas-visual-analysis,ExploriPy,Holoviews,lux,Dataprep,atoti,QuickDA ,panel-highcharts,Know Your Data,Atoti ,ExploriPy,autoplotter,tensorflow data validation,skimpy,Skim,OpenRefine,Visualizer,autoclean,Autoplotter,dataTile,mito,Bamboolib,TensorFlow Data Validation,speedML,edaviz,pandas-summary,ExploriPy, ipywidgets,ipympl,data_describe,lens,DStack,autoplotter,klib,Datasette,FACETS,TensorFlow Data Validation,Auto Data Exploration and Feature Recommendation Tool,great_expectations,DataProfiler,Datasette,streamlit-aggrid,Quick-EDA,QuickDA,Datatile,Deepnote,PiML,AutoPlotter,Klib,Pivottablejs,Qgrid,facets,Great Expectations,Explainerdashboard,BitRook,AutoPlotter,OmniXAI,tabloo,sidetable,HvPlot,summarytools,fasteda,Rath,Missingno,Sketch,pygwalker,fasteda,Apache Superset,Algorithm-visualizer,perspective,jupyter-datatables,dfgui,AutoProfiler,Datatile,ExploriPy Three R Libraries for Automated EDA dataMaid,DataExplorer,SmartEDA fiftyone Highly Interactive Dashboards For Visualizing Datasets and Interpret Model https://towardsdatascience.com/highly-interactive-dashboards-for-visualizing-dataset-and-interpret-model-ce6311ea57ca interpret Dashboards for Interpreting & Comparing Machine Learning Models https://towardsdatascience.com/dashboards-for-interpreting-comparing-machine-learning-models-ffcfb4c05152 QuickDA https://towardsdatascience.com/save-hours-of-work-doing-a-complete-eda-with-a-few-lines-of-code-45de2e60f257 Dataprep https://towardsdatascience.com/dataprep-eda-accelerate-your-eda-eb845a4088bc https://www.analyticsvidhya.com/blog/2021/05/dataprep-library-perform-eda-faster/ explainerdashboard https://towardsdatascience.com/the-quickest-way-to-build-dashboards-for-machine-learning-models-ec769825070d Facets https://github.com/PAIR-code/facets https://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc pywedge https://github.com/taknev83/pywedge https://towardsdatascience.com/automated-interactive-package-for-eda-modeling-and-hyperparameter-tuning-in-a-few-lines-of-228c561fa63c Datapane makes it simple to build shareable reports from Python https://github.com/datapane/datapane https://towardsdatascience.com/datapanes-new-features-create-a-beautiful-dashboard-in-python-in-a-few-lines-of-code-a3c44523292b https://towardsdatascience.com/introduction-to-datapane-a-python-library-to-build-interactive-reports-4593fd3cb9c8 lux https://medium.com/swlh/automating-exploratory-data-analysis-part-3-d04352b83072 https://pub.towardsai.net/speed-up-eda-with-the-intelligent-lux-37f96542527b lux Python API for Intelligent Visual Data Discovery https://github.com/lux-org/lux https://analyticsindiamag.com/python-guide-to-lux-an-interactive-visual-discovery/ Automatic EDA https://thecleverprogrammer.com/2021/02/06/automatic-eda-using-python/ Automated Interactive Package for EDA, Modeling, and Hyperparameter Tuning in a few lines of Python Code https://towardsdatascience.com/automated-interactive-package-for-eda-modeling-and-hyperparameter-tuning-in-a-few-lines-of-228c561fa63c Arena https://github.com/ModelOriented/Arena https://github.com/mstaniak/autoEDA-resources https://thecleverprogrammer.com/2021/02/06/automatic-eda-using-python/ ExploriPy import EDA-https://analyticsindiamag.com/hands-on-tutorial-on-exploripy-effortless-target-based-eda-tool/ Lens- Statistical Analysis of Data https://analyticsindiamag.com/hands-on-tutorial-on-lens-python-tool-for-swift-statistical-analysis/ Dashboard in Less Than 10 Lines of Code https://towardsdatascience.com/build-dashboards-in-less-than-10-lines-of-code-835e9abeae4b Plotly Express Interprete data through interactive visualization https://pub.towardsai.net/matplotlib-is-dead-long-life-to-plotly-express-e1671dce0d18 Rich terminal dashboards https://www.willmcgugan.com/blog/tech/post/building-rich-terminal-dashboards/ Explainable AI dashboards https://github.com/oegedijk/explainerdashboard https://www.youtube.com/watch?v=ZgypAMRcmw8 Machine Learning Model Dashboard https://towardsdatascience.com/machine-learning-model-dashboard-4544daa50848 Creating Automated Python Dashboards using Plotly, Datapane, and GitHub Actions https://towardsdatascience.com/creating-automated-python-dashboards-using-plotly-datapane-and-github-actions-ff8aa8b4e3 atoti Python library to quickly build BI analytics dashboards https://docs.atoti.io/latest/tutorial/tutorial.html interactive dashboards https://medium.com/analytics-vidhya/explainer-dashboard-build-interactive-dashboards-for-machine-learning-models-fda63e0eab9 MitoSheets https://analyticsindiamag.com/guide-to-mitosheets-harnessing-power-of-spreadsheets-in-python/ Datacleaner-https://analyticsindiamag.com/tutorial-on-datacleaner-python-tool-to-speed-up-data-cleaning-process/ Datacleaner :dora ,Voilà -Jupyter Notebooks quickly into standalone web applications , Plotly Dash - for more advanced and production level dashboards featurewiz(Select the best features from your data set fast with a single line of code) - https://github.com/AutoViML/featurewiz explainerdashboard https://medium.com/analytics-vidhya/explainer-dashboard-build-interactive-dashboards-for-machine-learning-models-fda63e0eab9 interpret Dashboards for Interpreting & Comparing Machine Learning Models https://hmix13.medium.com/dashboards-for-interpreting-comparing-machine-learning-models-ffcfb4c05152 https://www.kdnuggets.com/2019/07/10-simple-hacks-speed-data-analysis-python.html Panel - web apps Automating report generation with Jupyter Notebooks https://medium.com/applied-data-science/full-stack-data-scientist-5-automating-report-generation-with-jupyter-notebooks-919e32e88d18 10 Useful Jupyter Notebook Extensions for a Data Scientist https://towardsdatascience.com/10-useful-jupyter-notebook-extensions-for-a-data-scientist-bd4cb472c25e Datapane ( Build Interactive Reports) https://towardsdatascience.com/introduction-to-datapane-a-python-library-to-build-interactive-reports-4593fd3cb9c8 https://www.kdnuggets.com/news/index.html pomegranate probabilistic modelling in Python https://github.com/jmschrei/pomegranate https://www.kdnuggets.com/2020/12/fast-intuitive-statistical-modeling-pomegranate.html 16.CUPY (array process parallel in gpu) https://pypi.org/project/cupy/ 17.Dabl-automate the known 80% of Data Science which is data preprocessing, data cleaning, and feature engineering https://pypi.org/project/dabl/ 18.dask (parallel comptataion) https://docs.dask.org/en/latest/ https://medium.com/rapids-ai/reading-larger-than-memory-csvs-with-rapids-and-dask-e6e27dfa6c0f#cid=av01_so-nvsh_en-us pandarallel https://towardsdatascience.com/make-pandas-run-blazingly-fast-3dbcd621f75b Dask Dataframe and SQL https://docs.dask.org/en/latest/dataframe-sql.html Swiftapply  – Automatically efficient pandas apply operations https://www.kdnuggets.com/2018/04/swiftapply-automatically-efficient-pandas-apply-operations.html Dask CUDA Numba https://github.com/numba/numba https://www.youtube.com/watch?v=3O-Pvnrbsu0 https://www.analyticsvidhya.com/blog/2021/04/numba-for-data-science-make-your-py-code-run-1000x-faster/ Arrow https://towardsdatascience.com/how-fast-is-reading-parquet-file-with-arrow-vs-csv-with-pandas-2f8095722e94 Cython,Numba,PyPy,ray,loky,Dask,p_tqdm (aka Pathos + tqdm),modin,connectorx,cudf, cuML Reducing Pandas memory https://pythonspeed.com/articles/pandas-load-less-data/ https://www.youtube.com/watch?v=HNE0qHJ9A9o Speed up Scikit-Learn Model Training https://www.kdnuggets.com/2021/02/speed-up-scikit-learn-model-training.html mpire Python package for easy multiprocessing, but faster than multiprocessing https://github.com/Slimmer-AI/mpire thundergbm Fast GBDTs and Random Forests on GPUs https://github.com/Xtra-Computing/thundergbm thundersvm https://github.com/Xtra-Computing/thundersvm NumPy API on TensorFlow https://www.tensorflow.org/guide/tf_numpy https://www.youtube.com/watch?v=mgY46AEXnG0 change to proper dtypes,usecols of required only reduce size Better Data Storage : CSV,Parquet,fastparquet,Feather,lance,HDF5,Apache Arrow,Lance pandas chunksize,Pandas vectorization,Numpy Vectorization, multiprocessing,airflow,celery,Modin ,Vaex,ray,Dask,PyPolars,Polars,spark,pyspark,Koalas,Cython , cuML,cuDF,cupy,mars,ray,Caching,rapids,joblib,snorkel,arrow,Pyarrow,Ponder,Apache Arrow,Datatable,Fastparquet,dampr,Data Table , pandarallel ,Parallel-Pandas,numba,bolt, numexpr,ipython parallel,Nim,speedML,ConnectorX , apache arrow,jax,Pandas-on-Spark,Terality,swifter,partial_fit(),Numba,numexpr,mtalgDask,PyArrow, and PySpark,Fugue,NumPy vectorization,Pandas vectorization,datatable,RAPIDS,Swifter,taichi,scikit-learn-intelex,𝚏𝚞𝚐𝚞𝚎,bottleneck,Pandarallel,Datatable,Pyspark,Koalas,Cylon,Ibis,pandarallel,Blaze,Odo,multiprocessing,joblib,bottleneck,Mapply,Bottleneck,DuckDB,DataFusion, Blaze,Dremio,DuckDB,dbt,Ponder,Daft https://www.youtube.com/watch?v=eJyjB3cNIB0&feature=youtu.be deal with Big Data Optimize dataframes,Use only required columns,Chunking data,Sparse data formats,Better Data file formats(Parquet,Feather,HDF5),Pandas alternates(Modin,vaex,dask,spark),Intel(R) extension for sklearn, Apply Vectorized,Numba,Rapids cuDF composer library of algorithms to speed up neural network training https://github.com/mosaicml/composer ColossalAI A Unified Deep Learning System for Large-Scale Parallel Training https://github.com/hpcaitech/ColossalAI 19.dataprep (Understand your data with a few lines of code in seconds) data-preparation-tools - https://improvado.io/blog/data-preparation-tools 20.Dora library is another data analysis library designed to simplify exploratory data analysis. https://pypi.org/project/Dora/ 23.FlashText (A library faster than Regular Expressions for NLP tasks) https://pypi.org/project/flashtext/ 24.Guietta (tool that makes simple GUIs simple) https://pypi.org/project/guietta/ pandas-visual-analysis -https://analyticsindiamag.com/hands-on-guide-to-pandas-visual-analysis-way-to-speed-up-data-visualization/ 25.hummingbird (make code fastly exexcute) https://pypi.org/project/Hummingbird/ https://analyticsindiamag.com/guide-to-hummingbird-a-microsofts-library-for-expediting-traditional-machine-learning-models/ CUML- increase the speed of training your machine learning model https://towardsdatascience.com/train-your-machine-learning-model-150x-faster-with-cuml-69d0768a047a https://docs.rapids.ai/api/cuml/stable/ modin https://www.kdnuggets.com/2021/03/speed-up-pandas-modin.html Datatable speed up pandas https://www.youtube.com/watch?v=mQi6QIGGJ5U Process large datasets without running out of memory https://pythonspeed.com/memory/?utm_medium=email&utm_source=topic+optin&utm_campaign=awareness&utm_content=20210426+data+ai+nl&mkt_tok=MTA3LUZNUy0wNzAAAAF8rA-uJucI5nYkInNB60OO8SozgyRZZ2ptfW-Dt-5HR3I0ysFHju2OYpeK_JZRtxcnmHGSefwL-1zg9Be3zse6zZVklh3zcWYSCxLRvJqd5LfAJMaF Snap ML — Speed Up Model Training https://medium.com/ibm-data-ai/snap-ml-speed-up-model-training-2ef36fbbf101 26.memory-profiler (tell memory consumption line by line) https://pypi.org/project/memory-profiler/ Cython A Speed-Up Tool for your Python Function https://towardsdatascience.com/cython-a-speed-up-tool-for-your-python-function-9bab64364bfd PyPy Run Your Python Code as Fast as C https://towardsdatascience.com/run-your-python-code-as-fast-as-c-4ae49935a826 Python Tricks for Keeping Track of Your Data https://towardsdatascience.com/python-tricks-for-keeping-track-of-your-data-aef3dc817a4e 27.numexpr (incerease speed of execution of numpy) https://github.com/pydata/numexpr pypolars instead of pandas (beating-pandas-performance) https://www.youtube.com/watch?v=1-O_KnLZEso https://towardsdatascience.com/3x-times-faster-pandas-with-pypolars-7550e605805e 50X speed up your Pandas apply function https://github.com/jmcarpenter2/swifter sklearn 100x Faster https://www.kdnuggets.com/2019/09/train-sklearn-100x-faster.html JAX Autograd and XLA, facilitating high-performance machine learning research https://github.com/google/jax Numba (optimise performance of numpy and high performance python compiler) http://numba.pydata.org/ Pyston project open sources its faster Python https://www.infoworld.com/article/3618169/pyston-project-open-sources-its-faster-python.html 28.pandarallel (simple and efficient tool to parallelize your pandas computation on all your CPUs) https://pypi.org/project/pandarallel/ Pandarallel, Pandarallel’s parallel_apply() 29.PDFTableExtract(by PyPDF2) https://github.com/ashima/pdf-table-extract Camelot-https://towardsdatascience.com/extracting-tabular-data-from-pdfs-made-easy-with-camelot-80c13967cc88 30.PyImpuyte(Python package that simplifies the task of imputing missing values in big datasets) https://pypi.org/project/PyImpuyte/ 31.libra(Automates the end-to-end machine learning process in just one line of code) https://pypi.org/project/libra/ 32.debug code by puyton -m pdp -c continue 33.cURL (This is a useful tool for obtaining data from any server via a variety of protocols including HTTP.) https://stackabuse.com/using-curl-in-python-with-pycurl/ 34.csvkit https://pypi.org/project/csvkit/ 35.IPython IPython gives access to enhanced interactive python from the shell. 36.pip install faker (Create our own Dataset) https://pypi.org/project/Faker/ 37.Python debugger %pdb 38.𝚟𝚘𝚒𝚕𝚊-From notebooks to standalone web applications and dashboards https://voila.readthedocs.io/en/stable/ https://github.com/voila-dashboards/voila 39.𝚝𝚜𝚕𝚎𝚊𝚛𝚗 for timeseries data https://github.com/tslearn-team/tslearn 40.texthero text-based dataset in Pandas Dataframe quickly and effortlessly https://github.com/jbesomi/texthero 41.𝚔𝚊𝚕𝚎𝚒𝚍𝚘(web-based visualization libraries like your Jupyter Notebook with zero dependencies) https://pypi.org/project/kaleido/ 42.Vaex- Reading And Processing Huge Datasets in seconds https://github.com/vaexio/vaex 43.Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning https://eng.uber.com/introducing-ludwig/ 44.Google's TAPAS, a BERT-Based Model for Querying Tables Using Natural Language https://github.com/google-research/tapas 45.RAPIDS open GPU Data Science https://rapids.ai/ RAPIDS cuML,cudf tick is a lightweight machine learning library https://x-datainitiative.github.io/tick/ modular machine learning framework http://www.pybrain.org/docs/ machine learning framework It supports several programming languages notably: Python, R, Java, Scala, Ruby and Lua Shogun https://github.com/shogun-toolbox/shogun/ 46.pyforest Lazy-import of all popular Python Data Science libraries. Stop writing the same imports over and over again. https://pypi.org/project/pyforest/0.1.1/ 47.Modin Get faster Pandas with Modin https://github.com/modin-project/modin 48.Text2Code for Jupyter notebook - https://github.com/deepklarity/jupyter-text2code , https://towardsdatascience.com/data-analysis-made-easy-text2code-for-jupyter-notebook-5380e89bb493 49.Openrefine Tool-For Data Preprocessing Without Code https://analyticsindiamag.com/openrefine-tutorial-a-tool-for-data-preprocessing-without-code/ 50.Microsoft Releases Latest Version Of DeepSpeed deep learning optimisation library known as DeepSpeed- https://github.com/microsoft/DeepSpeed https://analyticsindiamag.com/microsoft-releases-latest-version-of-deepspeed-its-python-library-for-deep-learning-optimisation/ 51.4-pandas-tricks-https://towardsdatascience.com/4-pandas-tricks-that-most-people-dont-know-86a70a007993 53.autoplotter is a python package for GUI based exploratory data analysis-https://github.com/ersaurabhverma/autoplotter 54.3 NLP Interpretability Tools For Debugging Language Models-https://www.topbots.com/nlp-interpretability-tools/ 55.New Algorithm For Training Sparse Neural Networks (RigL)-https://analyticsindiamag.com/rigl-google-algorithm-neural-networks/ 56.Read Data from pdf and Word-PyPDF2,PDFMiner,PDFQuery,tabula-py,pdflib for Python,PDFTables,PyFPDF2 OpenCV to Extract Information From Table Images-https://analyticsindiamag.com/how-to-use-opencv-to-extract-information-from-table-images/ 57.Text Annotation-https://towardsdatascience.com/tortus-e4002d95134b 58.GDMix, A Framework That Trains Efficient Personalisation Models - https://analyticsindiamag.com/linkedin-open-sources-gdmix-a-framework-that-trains-efficient-personalisation-models/ 59.Learn Machine Learning Concepts Interactively-https://towardsdatascience.com/learn-machine-learning-concepts-interactively-6c3f64518da2 60.Folium, Python Library For Geographical Data Visualization-https://analyticsindiamag.com/hands-on-tutorial-on-folium-python-library-for-geographical-data-visualization/ 61.GPU Technology Conference (GTC) Keynote Oct 2020-https://www.youtube.com/watch?v=Dw4oet5f0dI&list=PLZHnYvH1qtOYOfzAj7JZFwqtabM5XPku1 62.jiant nlp task-https://github.com/nyu-mll/jiant 63.painted your machine learning model-https://koaning.github.io/human-learn/ 64.Vector AI-https://github.com/vector-ai/vectorai 65.NVIDIA NeMo(for Conversational AI)-https://github.com/NVIDIA/NeMo 66.Deep Learning Models Without Coding(DeepCognition)-https://analyticsindiamag.com/how-to-use-deepcognition-to-build-drag-and-drop-deep-learning-models-without-coding/ 67.100 Machine Learning Projects-https://medium.com/@amankharwal/100-machine-learning-projects-aff22b22dd6e 68.Question generation using Natural Language Processing-https://github.com/ramsrigouthamg/Questgen.ai 69.PixelLib(image segmentation,Blur Background,Gray Background,Background Colour Change,Background Change)-https://github.com/ayoolaolafenwa/PixelLib 70.High-Resolution 3D Human Digitization-https://shunsukesaito.github.io/PIFuHD/ 71.AI model that translates 100 languages without relying on English data - https://ai.facebook.com/blog/introducing-many-to-many-multilingual-machine-translation/ 72.800 free textbooks - https://open.umn.edu/opentextbooks 73.TensorDash is an application that lets you remotely monitor your deep learning model's metrics and notifies you when your model training is completed or crashed. https://github.com/CleanPegasus/TensorDash HyperDash https://towardsdatascience.com/how-to-monitor-and-log-your-machine-learning-experiment-remotely-with-hyperdash-aa7106b15509 74.YellowBrick -select features, tune hyperparameters, select the best models, and understand the performance metrics. 75.Freely Available Python Books-https://rajukumarmishrablog.com/freely-available-python-books/ Collection of Python Cheat Sheets- https://rajukumarmishrablog.com/collection-of-python-cheat-sheets/ 76.Add External Data to Your Pandas Dataframe - https://towardsdatascience.com/add-external-data-to-your-pandas-dataframe-with-a-one-liner-f060f80daaa4 https://www.openblender.io/#/welcome 77.visualize the model architecture-https://github.com/PerceptiLabs/PerceptiLabs 78.Train Conversational AI in 3 lines of code with NeMo and Lightning-https://towardsdatascience.com/train-conversational-ai-in-3-lines-of-code-with-nemo-and-lightning-a6088988ae37 79.Machine Learning for Healthcare by mit-https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/ 80.pydot is an interface to Graphviz ,AutoGraph-Easy control flow for graphs,Neo4j-Graph Data Science Library,pyRDF2Vec-Representations of Entities in a Knowledge Graph,igraph,NetworkX,euler,pyvis,NEuler: No-code graph algorithms,dgl ease deep learning on graph,Graph4nlp,Graph-tool,Networkit,Igraph PyG (PyTorch Geometric) Graph Neural Network Library for PyTorch https://github.com/pyg-team/pytorch_geometric 7 Open Source Libraries for Deep Learning Graphs https://www.kdnuggets.com/2021/07/7-open-source-libraries-deep-learning-graphs.html GeometricFlux.jl,PyTorch GNN, Jraph,Spektral,Graph Nets,Deep Graph Library , PyTorch Geometric https://www.tensorflow.org/neural_structured_learning https://github.com/deepmind/graph_nets https://deepmind.com/research/open-source/graph-nets-library https://www.kdnuggets.com/2019/09/5-graph-algorithms-data-scientists-know.html https://towardsdatascience.com/visualizing-networks-in-python-d70f4cbeb259 Pyviz https://towardsdatascience.com/interactive-network-visualization-757af376621 AutoGL: The First Ever AutoML Framework for Graph Datasets https://analyticsindiamag.com/meet-autogl-the-first-ever-automl-framework-for-graph-datasets/ https://analyticsindiamag.com/complete-guide-to-autogl-the-latest-automl-framework-for-graph-datasets/ http://mn.cs.tsinghua.edu.cn/AutoGL/ Graph Neural Networks, PySpark, Neural Cellular Automata, FB Prophet, Google Cloud and NLP codes https://github.com/RubensZimbres/Repo-2021 AmpliGraph: A Machine Learning Library For Knowledge Graphs https://analyticsindiamag.com/guide-to-ampligraph-a-machine-learning-library-for-knowledge-graphs/ open-source project for analysis of graphs or networks GrasPy / graspologic https://graspy.neurodata.io/ Pykg2vec: A Python Library for Knowledge Graph Embedding https://analyticsindiamag.com/pykg2vec/ https://www.kdnuggets.com/2019/05/60-useful-graph-visualization-libraries.html https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html 84.Google Introduces Document AI (DocAI) https://www.marktechpost.com/2020/11/05/google-introduces-document-ai-docai-platform-for-automated-document-processing/ 85.100 Machine Learning Projects-https://amankharwal.medium.com/100-machine-learning-projects-aff22b22dd6e 86.https://towardsdatascience.com/25-hot-new-data-tools-and-what-they-dont-do-31bf23bd8e56 87.Opacus: A high-speed library for training PyTorch models-https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy 88.lazynlp https://github.com/chiphuyen/lazynlp 90.Pseudo-Labeling (deal with small datasets)https://towardsdatascience.com/pseudo-labeling-to-deal-with-small-datasets-what-why-how-fd6f903213af 91.Project List A - Comparatively Easy Wine Quality Analysis,Boston Housing Prediction,Spam Email Classification,Survival Prediction - Titanic Disaster,Stock Market Prediction Class of Flower Prediction,Bigmart Sales Prediction,Air Pollution Prediction,IMDB Prediction,Optimizing Product Price,Web Traffic Time Series Forecasting,Insurance Purchase Prediction,Tweet Classification Project List B - Comparatively Difficult,Domain-Specific Chatbot,Fake News Detection,Human Action Recognition,Video Classification,Driver Drowsiness Detection,Medical Report Gen Using CT Scans,Sign Language Detection,Image Caption Generator,Celebrity Voice Prediction,Speech Emotion Recognition,Job Recommendation System,Interest Level in Rental Properties,Google Ads Keywords Generator https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/ https://ml-showcase.paperspace.com/ https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code https://dev.to/hb/30-machine-learning-ai-data-science-project-ideas-gf5 https://www.theinsaneapp.com/2021/01/top-30-ai-and-ml-projects-for-2021.html https://medium.com/coders-camp/180-data-science-and-machine-learning-projects-with-python-6191bc7b9db9 https://www.analyticsvidhya.com/blog/2020/12/10-data-science-projects-for-beginners/?utm_source=linkedin&utm_medium=AV|link|high-performance-blog|blogs|44195|0.375 https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392 https://medium.com/coders-camp/96-python-projects-with-source-code-4069eb58beef https://thecleverprogrammer.com/machine-learning/ https://www.kdnuggets.com/2020/03/20-machine-learning-datasets-project-ideas.html https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/?utm_source=linkedin&utm_medium=KJ|link|blackbelt|blogs|44081|0.625 https://www.kdnuggets.com/2021/03/10-amazing-machine-learning-projects-2020.html?utm_content=bufferc38bd&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer https://data-flair.training/blogs/machine-learning-datasets/# https://data-flair.training/blogs/machine-learning-project-ideas/ https://data-flair.training/blogs/artificial-intelligence-ai-tutorial/ https://www.theinsaneapp.com/2020/11/data-science-projects-with-source-code.html https://data-flair.training/blogs/cartoonify-image-opencv-python/ https://data-flair.training/blogs/python-project-calorie-calculator-django/ https://www.theinsaneapp.com/2020/11/machine-learning-projects-with-source-codes.html https://www.theinsaneapp.com/2020/11/data-science-projects-with-source-code.html https://amankharwal.medium.com/20-machine-learning-projects-on-future-prediction-with-python-93932d9a7f7f https://medium.com/coders-camp/20-deep-learning-projects-with-python-3c56f7e6a721 https://amankharwal.medium.com/12-machine-learning-projects-on-object-detection-46b32adc3c37 https://amankharwal.medium.com/7-python-gui-projects-for-beginners-87ae2c695d78 https://github.com/Kushal997-das/Project-Guidance https://amankharwal.medium.com/20-machine-learning-projects-for-portfolio-81e3dbd167b1 https://amankharwal.medium.com/4-chatbot-projects-with-python-5b32fd84af37 https://amankharwal.medium.com/30-python-projects-solved-and-explained-563fd7473003 https://www.aiquotient.app/projects https://www.aiquotient.app/ https://www.mltut.com/best-machine-learning-projects-for-beginners/ https://medium.com/coders-camp/20-machine-learning-projects-on-nlp-582effe73b9c 93.The Linux Command Handbook-https://www.freecodecamp.org/news/the-linux-commands-handbook/ 94.130 Machine Learning Projects Solved and Explained-https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392 95.DataBrew-do drag-and-drop data cleansing 96.stratascratch- https://www.stratascratch.com/ 97.5 ways to celebrate TensorFlow's 5th birthday-https://blog.google/technology/ai/5-ways-celebrate-tensorflows-5th-birthday/ 98.TensorFlow.js: Machine Learning in Javascript https://blog.tensorflow.org/2018/03/introducing-tensorflowjs-machine-learning-javascript.html 99.Language Interpretability Tool open-source platform for visualization and understanding of NLP models - https://pair-code.github.io/lit/ 100.Deep Learning Hardware Guide https://towardsdatascience.com/another-deep-learning-hardware-guide-73a4c35d3e86 101.johnsnowlabs- https://nlp.johnsnowlabs.com/ https://nlp.johnsnowlabs.com/docs/en/quickstart https://nlp.johnsnowlabs.com/docs/en/licensed_release_notes 104.Clarifai-https://www.clarifai.com/ https://analyticsindiamag.com/clarifai/ 105.rapidly build and deploy machine learning models https://analyticsindiamag.com/top-10-datarobot-alternatives-one-must-know/ 106.Hive Data full-stack AI https://thehive.ai/hive-data 107.real-time remote service to get the Keras callbacks to the telegram including the details of metrics https://github.com/ksdkamesh99/TensorGram 108.Language Interpretability Tool - https://pair-code.github.io/lit/demos/ 109.Docly will handle the comments http://thedocly.io/ 110.machine-learning-roadmap-2020 https://whimsical.com/machine-learning-roadmap-2020-CA7f3ykvXpnJ9Az32vYXva 112.freecodecamp - https://www.freecodecamp.org/learn 113.image_to_string (pytesseract) Extract Tables in PDFs to pandas DataFrames - tabula-py 114.NLP Pipelines in a single line of code https://medium.com/analytics-vidhya/nlp-pipelines-in-a-single-line-of-code-500b3266ac7b 116.aitextgen #for ai text generation 117.http://introtodeeplearning.com/ http://cs231n.stanford.edu/ http://web.stanford.edu/class/cs224n/index.html#schedule https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5 117.https://data-flair.training/blogs/data-science-tutorials-home 119.Pystiche - Create Your Artistic Image Using Pystiche https://analyticsindiamag.com/pystiche/ https://pystiche.readthedocs.io/en/latest/index.html 120.Low Light Image Enhancement using Python & Deep Learning https://github.com/soumik12345/MIRNet/ https://www.youtube.com/watch?v=b5Uz_c0JLMs 121.Koalas on Apache Spark - Pandas API https://www.youtube.com/watch?v=kOtAMiMe1JY&t=482s https://koalas.readthedocs.io/en/latest/ 122.DALL·E https://openai.com/blog/dall-e/ https://analyticsindiamag.com/comprehensive-guide-to-dall-e-by-openai-creating-images-from-text/ https://github.com/lucidrains/big-sleep https://github.com/lucidrains/deep-daze https://www.youtube.com/watch?v=lVR5kN7SjQ8&feature=youtu.be DALL·E Mini,GPT-3,Dalle-2,Dalle-3,Imagen,RE-IMAGEN,Parti,Midjourney,Craiyon,Make-A-Scene,Imagen,DALL-E,Imagen, NUWA-Infinity,Make a Scene,Cogview 2,VQGAN,VQGAN-Clip,Latent-Diffusion,Parti,MidJourney,Ultraleap’s Midjourney, Hugging Face’s Craiyon, Meta’s Make-A-Scene and Google’s Imagen,CogVideo,Big Sleep,Disco,Stable Diffusion,fast-stable-diffusion,DreamStudio,CodeFormer,DreamBooth,Tiktok’s Greenscreen,textual_inversion,GauGAN2,Stable-Craiyon,Disco Diffusion,DreamBooth,AI Greenscreen,Wonder,Nightcafe,Midjourney, craiyon,loab,Starry AI,Dream By,Wombo,Nightcafe,Pixray,Deep Dream,Stable Diffusion,DreamFusion,Make-A-Video,Imagen Video,Midjourney,CogVideo,ERNIE-ViLG 2.0,eDiffi,pixray,starryai,promptoMANIA,starry.ai,NightCafe,Artbreeder,wombo.ai,Muse,BlueWillow,StyleGAN-T,GigaGAN,DeepFloyd IF, Bing Image Creator,Craiyon,InstantArt,Pixray,Blue Willow,Playground AI,Picsart,Perfusion AI,XGen-Image,Ideogram AI,DeciDiffusion,lexica https://pharmapsychotic.com/tools.html https://airtable.com/shrDxAxCCxAZVtMnt/tbl3FzgFjvvuYZMm9 https://www.marktechpost.com/2022/10/05/top-artificial-intelligence-ai-based-text-to-image-generators/ text to video,images,audio,3D: Adobe firefly,NVIDIA Picasso,Runway text to video : CogVideo,Make-A-Video,Phenaki,Imagen Video,DreamFusion,Phenak,CogVideo,GODIVA,NÜWA,Google UniTune (fine-tuned Imagen),Synthesia,Lumen5,Flixclip,Elai,Veed.io,Kaiber,Genmo,LeiaPix,Glia Cloud,Stable Diffusion Videos,Synthesia,InVideo,Lumen5,Designs.ai,Pictory,Wisecut,Veed.io,Fliki,Shap-e,dalle,pointe,AdaMPI,AudioGen 3D Models from Text : DreamFusion,CLIP-Mesh,Point-E,Magic3D,Text2Mesh,CLIP-Mesh,Neuralangelo Text-to-Audio : Audiogen,diffsound,GliaCloud,Synthesia,InVideo,Synths Video,VEED.IO,Lumen5,Pictory,Designs.ai,Wisecut,Replica,Speechify,Murf,Play.ht,Lovo.ai,VALL-E,VALL-E X,MusicLM, SingSong, Moûsai 2, AudioLDM, and EPIC-SOUND,Audio-LDM Top 12 AI Music Generators :MusicLM – Google’s Text to Music Generator,Soundraw.io,Amper Music,AIVA,Humtap,Amadeus Code,Computoser,Google’s Magenta ,Chrome’s Song Maker,Generative.FM,MuseNet Text-to-Motion : MotionCLIP,Language2Pose Text-to-PowerPoint : ChatBCG Mubert Text to Music https://github.com/MubertAI/Mubert-Text-to-Music ,MusicLM,MusicGen Music generator AIVA,Amper AI,Jukebox,Soundraw,Evoke, AudioML,EnCodec Text generators Frase Io,Peppertype,Rytr,Jasper,Copy.ai,ChatGPT Beginner’s Guide to the CLIP Model https://www.kdnuggets.com/2021/03/beginners-guide-clip-model.html https://www.kdnuggets.com/2021/03/multilingual-clip--huggingface-pytorch-lightning.html StyleCLIP: Text Driven Image Manipulation https://analyticsindiamag.com/guide-to-styleclip-text-driven-image-manipulation/ https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html 123.SpeechBrain https://speechbrain.github.io/ 124.Real-Time High-Resolution Background Replacement https://analyticsindiamag.com/introducing-real-time-high-resolution-background-replacement/ https://github.com/PeterL1n/BackgroundMattingV2 125.greppo Build & deploy geospatial applications quick and easy. https://github.com/greppo-io/greppo 126.Online tools to create mind-blowing AI art https://analyticsindiamag.com/online-tools-to-create-mind-blowing-ai-art/ **** If you like my work. please buy me a coffee it motivate me -> https://www.buymeacoffee.com/achuthasubhash?new=1 **** HAPPY LEARNING