AI Learning Kit

A curated collection of AI learning materials

🗺️ Roadmaps

• 📍  AI Engineer Roadmap — Step-by-step guide to building modern AI applications and working with LLMs
• 📍  ML Engineer Roadmap — Structured path covering core ML algorithms, model training, & MLOps
• 📍  Data Engineer Roadmap — Complete guide to data pipelines, databases, & preparing data for AI workflows

🚀 Introduction to AI

• 🎥  AI For Everyone — Andrew Ng's non-technical intro to AI concepts and strategy
• 📗  Elements of AI — Introduction to AI basics by the University of Helsinki

🧮 Maths for AI

• 🎥  The Math Behind AI — DeepLearning.AI specialization on math essentials
• 🎥   Khan Academy — Foundational math courses covering all AI prerequisites
  • Linear Algebra
  • Calculus
  • Statistics and Probability
• 📗  Mathematics for Machine Learning — Free textbook by Deisenroth, Faisal, and Ong

🐍 Python for AI

• 🎬  Python for Everybody — Dr. Chuck's Python specialization for absolute beginners
• 🎥  AI Python for Beginners — DeepLearning.AI course for those with programming knowledge, focused on AI

💻 AI Foundations

• 🎥  CS50's Intro to AI with Python — Harvard's AI course covering search, knowledge, and learning
• 🎬  Udacity AI Nanodegree — Project-based AI program with mentorship

✍️ Prompt Engineering

• 🎥  ChatGPT Prompt Engineering for Developers — DeepLearning.AI course with Isa Fulford and Andrew Ng
• 📗  Prompt Engineering Guide — DAIR.AI's comprehensive and community-driven guide
• 📗  OpenAI Prompt Engineering Docs — Official best practices from OpenAI

📚 Python Libraries for AI

• 🎬  The Numpy Stack in Python — Lazy Programmer's course on numpy, pandas & matplotlib
• 🎬  Introduction to Data Science in Python — University of Michigan course on Coursera
• 📗  Scikit-learn MOOC - Machine learning in Python with scikit-learn
• 📗  Data Analysis with Python — freeCodeCamp certification with projects

🤖 Machine Learning

• 🎥  Machine Learning Specialization — Andrew Ng's foundational 3-course series on Coursera
• 🎥  Google's Machine Learning Crash Course — Fast-paced intro with TensorFlow exercises
• 📘  Hands-On Machine Learning with Scikit-Learn and PyTorch — Aurélien Géron's practical ML guide
• 📘  The 100-Page ML Book — Andriy Burkov's concise ML reference
• 📗  Python Data Science Handbook — Jake VanderPlas's free guide to the Python data science stack

🛠️ Machine Learning Frameworks

• 🎬  PyTorch for Deep Learning — Professional PyTorch Certificate by Coursera
• 🎥  TensorFlow in Practice — Professional TensorFlow Certificate by Coursera
• 📗  Hugging Face Transformers — Industry-standard library for state-of-the-art NLP models
• 🎥  LangChain for LLM App Development — DeepLearning.AI short course on LangChain fundamentals

🧠 Deep Learning

• 🎥  Deep Learning Specialization — Andrew Ng's 5-course deep learning series
• 📗  Practical Deep Learning — fast.ai's top-down, code-first approach to deep learning
• 🎥  Neural Networks: Zero to Hero — Andrej Karpathy's series explaining math & code behind neural networks
• 🎥  MIT 6.S191: Introduction to Deep Learning — MIT's flagship intro to deep learning
• 📗  Deep Learning Book — The "bible of deep learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• 📗  Dive into Deep Learning — Interactive textbook with code in PyTorch, TensorFlow, and JAX

✨ Generative AI & Large Language Models (LLMs)

• 🎥  Generative AI for Everyone — Andrew Ng's non-technical intro to generative AI
• 🎥  Generative AI with LLMs — DeepLearning.AI + AWS course on training and deploying LLMs
• 📗  Hugging Face LLM Course — End-to-end course on building with LLMs
• 📁  LLM Course — Comprehensive roadmap and hands-on notebooks for LLM science and engineering
• 📘  Hands-On Large Language Models — Visual, practical guide to building with LLMs
• 🎥  Retrieval Augmented Generation — Build RAG systems with LLMs
• 📁  RAG Techniques — Comprehensive collection of RAG strategies with code, illustrations, and evaluations

🕵️ Agentic AI

• 🎥  Agentic AI — Comprehensive course on building agentic systems with iterative, multi-step workflows
• 📗  Introduction to AI Agents — Hugging Face's comprehensive course on building & deploying agents
• 📁  GenAI Agents — 50+ tutorials and implementations for AI agents, from basic bots to multi-agent systems
• 📁  AI Agents for Beginners — Microsoft's 12-lesson curriculum for getting started with AI agents
• 🎥  5-Day AI Agents Intensive — Project-based short course for building production-ready agents

🏗️ MLOps

• 🎥  Machine Learning Engineering in Production (MLOps) — Specialization on the end-to-end ML lifecycle
• 📘  Designing Machine Learning Systems — Chip Huyen's foundational text on MLOps architecture & strategy
• 📘  Practical MLOps — Noah Gift's guide to operationalizing ML with cloud-native tools
• 📁  MLOps Zoomcamp — Hands-on course covering experiment tracking, pipelines, and monitoring

🛠️ AI Engineering

• 📘  AI Engineering — Chip Huyen's guide to building AI applications in production
• 📁  Made With ML — End-to-end ML engineering basics to production
• 📗  Full Stack Deep Learning — The "missing semester" of ML: infrastructure, deployment, and testing
• 📁  ML Engineering — Practical guide to hardware, memory optimization, and scaling

📺 Essential YouTube Channels

• 🎥  3Blue1Brown — Visual explanations of math and neural networks
• 🎥  Andrej Karpathy — Deep technical dives into LLMs and Neural Networks
• 🎥  Sentdex — Practical Python AI projects and Neural Networks from scratch
• 🎥  StatQuest with Josh Starmer — Breaking down complex Statistics and ML concepts
• 🎥  Krish Naik — Practical ML/DL and career guidance
• 🎥  Codebasics — Project-based learning and data science fundamentals
• 🎥  CampusX — Comprehensive data science and machine learning tutorials
• 🎥  Stanford Online - Academic lecture series on world-class foundational courses in AI

📂 Learning Repos

• 📁  AI For Beginners — Microsoft's 12-week, 24-lesson comprehensive AI curriculum
• 📁  ML For Beginners — Microsoft's 12-week, 26-lesson curriculum on classical machine learning
• 📁  Generative AI for Beginners — Microsoft's 21-lesson course on building GenAI applications
• 📁  LLMs from scratch — Build a GPT-style LLM from the ground up with Sebastian Raschka
• 📁  Homemade Machine Learning — Python implementations of ML algorithms with math explanations
• 📁  Transformers Tutorials — Practical notebooks for using Hugging Face Transformers

🏋️ Practice Repos

• 📁  100 NumPy Exercises — Progressive NumPy challenges with solutions
• 📁  Deep Learning with Python Notebooks — Interactive labs and code for the Keras/TensorFlow ecosystem
• 📁  PyTorch Deep Learning Exercises — Practical modules and challenges for mastering PyTorch

🗂️ Datasets & Models

• 🌐  Hugging Face Models & Datasets — The largest open hub of datasets and ML models
• 🌐  OpenML — Open platform for discovering datasets and sharing reproducible ML experiments
• 🌐  Kaggle Datasets — Massive repository of community-published public datasets
• 🌐  Papers With Code — Free and open resource for ML papers, code, datasets, & evaluation tables
• 🌐  UCI ML Repository — The "grandfather" of ML datasets, widely used for benchmarking classical algorithms

🏆 Challenges & Competitions

• 🚉  Kaggle Competitions — Real-world ML competitions with datasets and leaderboards
• 🚉  LeetCode Pandas Challenges — Practice data manipulation with Pandas problems
• 🚉  StrataScratch — Data science interview questions from top companies
• 🚉  Deep-ML — Hands-on ML coding challenges with real datasets
• 🚉  NeetCode ML — Implement core ML algorithms from scratch with video explanations
• 🚉  MLExpert — ML coding interview questions with in-depth video explanations
• 🚉  TensorTonic — Implement 200+ ML papers and algorithms from scratch
• 🚉  DataInterview — Practice SQL and Python for data science interviews

🌐 Learning Platforms

• 🌐  DeepLearning.AI — Top-tier specializations and short courses curated by Andrew Ng
• 🌐  Hugging Face Learn — Professional tutorials and documentation for the Transformers ecosystem
• 🌐  Udemy AI Courses — Library of community-created AI courses for all tools and levels
• 🌐  Educative — Interactive, text-based environments for efficient skill building
• 🌐  DataCamp — Highly interactive, browser-based data science and AI fundamentals

Contributing

Contributions are always welcome! Whether it's adding new quality resources or suggesting a new category, join us in making this the ultimate AI guide. Read the guidelines

License

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