Resource Bank of Computer Vision, Natural Langauge Processing and MLops
This initiative compiles educational resources on a daily basis, making it possible for users to get all the data they want in one place.
📝 Table of Contents
♾️ 95% Data Science Skills Covered Course
| Sr.No | Courses | Link |
|---|---|---|
| 1 | Machine Learning Specialization |  |
| 2 | Deep Learning Specialization | |
| 3 | Natural Language Processing Specialization | |
| 4 | TensorFlow Developer Professional Certificate | |
| 5 | TensorFlow: Advanced Techniques Specialization | |
| 6 | TensorFlow: Data and Deployment Specialization | |
| 7 | Machine Learning Engineering for Production (MLOps) Specialization | |
| 8 | Generative Adversarial Networks (GANs) Specialization | |
| 9 | Practical Data Science on the Amazon Web Services (AWS) Cloud Specialization | |
| 10 | Mathematics of Data Science | |
| 11 | []() |
Extra Resources 
| Sr.No | Resource Name | Link |
|---|---|---|
| 1 | Intro to optimization in deep learning: Gradient Descent |  |
| 2 | ML system design usecases | |
| 2 | Free datasets for Data Science, Data Analytics, and ML projects | |
| 3 | Accuracy and Loss: Things to Know about The Top 1 and Top 5 Accuracy | |
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👁️🗨️ COMPUTER VISION
Computer Vision Learning Path Recommended by OpenCV
Books
| Sr.No | Book Name | Link |
|---|---|---|
| 1 | Computer Vision: Algorithms and Applications |  |
| 2 | Practical Deep Learning for Cloud, Mobile & Edge | |
| 3 | Concise Computer Vision: An Introduction into Theory and Algorithms | |
| 4 | Computer Vision: Principles, Algorithms, Applications, Learning | |
| 5 | Computer Vision: Models, Learning, and Inference | |
| 6 | Deep Learning for Vision Systems | |
| 7 | Modern Computer Vision with Pytorch | |
| 8 | Multiple View Geometry in Computer Vision | |
| 9 | Learning OpenCV 5 Computer Vision with Python 3 | |
| 10. | Computer Vision Metrics: Survey, Taxonomy, and Analysis | |
Articles 
| Sr.No | Article Name | Link |
|---|---|---|
| 1 | Computer Vision: Algorithms and Applications | [ ]() |
| 2 | End to End Learning for Self-Driving Cars using python | |
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Video 
| Sr.No | Video Name | Link |
|---|---|---|
| 1 | Stanford Computer Vision playlist |  |
| 2 | Deep Learning for Computer Vision | |
| 3 | Computer Vision (Andreas Geiger) | |
| 4 | Deep Learning with PyTorch: Zero to GANs | |
| 5 | Digital Image Processing | |
| 6 | Image Signal Processing | |
| 7 | The Geometry of vision | |
| 8 | UCF Computer Vision Video Lectures 2012 | |
| 9 | CAP5415 Computer Vision - Fall 2021 | |
| 10 | UCF CRCV | |
| 11 | Stanford Computer Vision | |
| 12 | Deep Learning for Computer Vision | |
| 13 | Deep Learning for Computer Vision | |
| 14 | Image Processing with C++ | |
| 15 | Full Stack Deep Learning - 2022 | |
| 16 | []() |
Extra Resources
| Sr.No | Resource Name | Link |
|---|---|---|
| 1 | Making Friends with Machine Learning | |
| 2 | End to End Tutorial on CNN | |
| 3 | Compact GPU Powerhouse | |
| 4 | Machine Learning from Scratch - Python Tutorials | |
| 5 | CUDA Crash Course | |
| 6 | Object Tracking Using Deep SORT and YOLOv4 | |
| 7 | Protecting Your Machine Learning Against Drift | |
| 8 | LeetCode | |
| 9 | Mathematics of Data Science | |
| 10 | Modern C++ (2021 Lecture & Tutorials) | |
| 11 | Lecture: Modern C++ (Summer 2018, Uni Bonn) | |
| 12 | Machine Learning | |
| 13 | Krish naik youtuber | |
| 14 | NPTEL MOOC Machine Learning 2016 | |
| 15 | Deep Learning | |
| 16 | 3Blue1Brown: Calculus | |
| 17 | Learn TensorFlow and Deep Learning fundamentals with Python | |
| 18 | GTC Sept 2022 Keynote with NVIDIA CEO Jensen Huang | |
| 19 | ML Model Training and Inference with a Data Mesh | |
| 20 | Attitude-Guided Loop Closure for Cameras with Negative Plane | |
| 21 | CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision | |
| 22 | LearnopenCV | |
| 23 | Expanding Language-Image Pretrained Models for General Video Recognition | |
| 24 | Open-Set Semi-Supervised Object Detection | |
| 25 | How To Deal with Dataset Bias | |
| 26 | []() |
📝 NATURAL LANGUAGE PROCESSING
Learning Path NLP:
Credit : graykode
Books 
| Sr.No | Book Name | Link |
|---|---|---|
| 1 | Natural Language Processing with Transformers, Revised Edition by Lewis Tunstall, Leandro von Werra, Thomas Wolf |  |
| 2 | Natural Language Processing with PyTorch Book by Brian McMahan and Delip Rao | |
| 3 | Transformers for Natural Language Processing by Denis Rothman, Antonio Gulli | |
| 4 | Mastering Transformers by Savaş Yıldırım , Meysam Asgari-Chenaghlu | |
| 5 | Advanced Natural Language Processing with TensorFlow 2 by Ashish Bansal | |
| 6 | Python Natural Language Processing Cookbook By Zhenya Antić | |
| 7 | Getting Started with Google BERT By Sudharsan Ravichandiran | |
| 8 | Exploring GPT-3 By Steve Tingiris | |
| 9 | Applied Natural Language Processing in the Enterprise by Ankur A. Patel, Ajay Uppili Arasanipalai | |
| 10 | Natural Language Processing in Action, Second Edition by Hobson Lane and Maria Dyshel | |
Articles
| Sr.No | Article Name | Link |
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| 1 | Computer Vision: Algorithms and Applications | []() |
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Video
| Sr.No | Video Name | Link |
|---|---|---|
| 1 | Natural Language Processing (University of Michigan) | |
| 2 | Stanford CS224N NLP with Deep Learning | |
| 3 | Chatbot by Binod Suman Academy | |
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Extra Resources
| Sr.No | Resource Name | Link |
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| 1 | Designing an ML Minded Product | |
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👨💻 MLOPS
Credit : ML-ops.org
Books
| Sr.No | Book Name | Link |
|---|---|---|
| 1 | Engineering MLOps: By Emmanuel Raj | |
| 2 | Machine Learning Design Patterns Book by Michael Munn, Sara Robinson, and Valliappa Lakshmanan | |
| 3 | Designing Machine Learning Systems Book by Chip Huyen | |
| 4 | Practical MLOps by Noah Gift, Alfredo Deza | |
| 5 | MLOps Engineering at Scale Book by Carl Osipov | |
| 6 | Machine Learning on Kubernetes by Faisal Masood, Ross Brigoli | |
| 7 | Machine Learning Engineering with MLflow By Natu Lauchande | |
| 8 | Machine Learning Engineering with Python By Andrew P. McMahon | |
| 9 | Kubeflow for Machine Learning by Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko | |
| 10. | Production-Ready Applied Deep Learning By Tomasz Palczewski , Jaejun (Brandon) Lee , Lenin Mookiah | |
Articles
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Video
| Sr.No | Video Name | Link |
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| 1 | MLOps Zoomcamp | |
| 2 | Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng | |
| 3 | Docker Tutorial in Hindi 2022 | |
| 4 | CS 329S: Machine Learning Systems Design | |
| 5 | Full Stack Deep Learning 2019 | |
| 6 | MLOps - Machine Learning Operations | |
| 7 | MLOps: ML Deployment 2020 | |
| 8 | Mlops Live Webinar | |
| 9 | Azure MLops | |
| 10 | MLOps by Pragmatic AI Labs | |
| 11 | MLops Tutorial by DVC.org | |
| 12 | Kubernetes Tutorial for Beginners [FULL COURSE in 4 Hours] | |
| 13 | Docker Tutorials For Beginner - 2 Million view | |
Extra Resources
| Sr.No | Resource Name | Link |
|---|---|---|
| 1 | Awesome MLops | |
| 2 | MadewithML | |
| 3 | Awesome Production Machine learning | |
| 4 | MLOps - Best Blog(Neptune.ai) | |
| 5 | OPERATIONALIZING MACHINE LEARNING | |
| 6 | Practical Guide of MLOps by Google | |
| 7 | Awesome MLOps Guide Tools | |
| 8 | MLops Blogs by MLOps BootCamp | |
| 9 | FedML MLOps Introduction | |
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🙏 Thanks for Reading 🙏 More is coming stay tune ⛹️♂️