README

📺 ML YouTube Courses

At DAIR.AI we ❤️ open AI education. In this repo, we index and organize some of the best and most recent machine learning courses available on YouTube.

Machine Learning

- Caltech CS156: Learning from Data
- Stanford CS229: Machine Learning
- Making Friends with Machine Learning
- Applied Machine Learning
- Introduction to Machine Learning (Tübingen)
- Machine Learning Lecture (Stefan Harmeling)
- Statistical Machine Learning (Tübingen)
- Probabilistic Machine Learning
- MIT 6.S897: Machine Learning for Healthcare (2019)

Deep Learning

- Neural Networks: Zero to Hero
- MIT: Deep Learning for Art, Aesthetics, and Creativity
- Stanford CS230: Deep Learning (2018)
- Introduction to Deep Learning (MIT)
- CMU Introduction to Deep Learning (11-785)
- Deep Learning: CS 182
- Deep Unsupervised Learning
- NYU Deep Learning SP21
- Foundation Models
- Deep Learning (Tübingen)

Scientific Machine Learning

- Parallel Computing and Scientific Machine Learning

Practical Machine Learning

- LLMOps: Building Real-World Applications With Large Language Models
- Evaluating and Debugging Generative AI
- ChatGPT Prompt Engineering for Developers
- LangChain for LLM Application Development
- LangChain: Chat with Your Data
- Building Systems with the ChatGPT API
- LangChain & Vector Databases in Production
- Building LLM-Powered Apps
- Full Stack LLM Bootcamp
- Full Stack Deep Learning
- Practical Deep Learning for Coders
- Stanford MLSys Seminars
- Machine Learning Engineering for Production (MLOps)
- MIT Introduction to Data-Centric AI

Natural Language Processing

- XCS224U: Natural Language Understanding (2023)
- Stanford CS25 - Transformers United
- NLP Course (Hugging Face)
- CS224N: Natural Language Processing with Deep Learning
- CMU Neural Networks for NLP
- CS224U: Natural Language Understanding
- CMU Advanced NLP 2021/2022/2024
- Multilingual NLP
- Advanced NLP

Computer Vision

- CS231N: Convolutional Neural Networks for Visual Recognition
- Deep Learning for Computer Vision
- Deep Learning for Computer Vision (DL4CV)
- Deep Learning for Computer Vision (neuralearn.ai)

Reinforcement Learning

- Deep Reinforcement Learning
- Reinforcement Learning Lecture Series (DeepMind)
- Reinforcement Learning (Polytechnique Montreal, Fall 2021)
- Foundations of Deep RL
- Stanford CS234: Reinforcement Learning

Graph Machine Learning

- Machine Learning with Graphs (Stanford)
- AMMI Geometric Deep Learning Course

Multi-Task Learning

- Multi-Task and Meta-Learning (Stanford)

Others

- MIT Deep Learning in Life Sciences
- Self-Driving Cars (Tübingen)
- Advanced Robotics (Berkeley)

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 Caltech CS156: Learning from Data

An introductory course in machine learning that covers the basic theory, algorithms, and applications.

- Lecture 1: The Learning Problem
- Lecture 2: Is Learning Feasible?
- Lecture 3: The Linear Model I
- Lecture 4: Error and Noise
- Lecture 5: Training versus Testing
- Lecture 6: Theory of Generalization
- Lecture 7: The V