ML-University

Why we included this project

Self-taught machine learning students spend a lot of time just finding good courses, and this index does that searching for them. It gathers free, high-quality material from MIT, Stanford, Berkeley, fast.ai, and Google, arranged by stage of learning so you start with the math foundation before moving through classical machine learning and deep learning, then on to NLP, reinforcement learning, and large language models. People already working in the field will find useful sections on running ML in production, generative and agentic AI, datasets, and must-read research papers. The real value is the curating: you jump from a list entry straight to the exact resource, which makes it easy to answer 'what should I study next' without the usual web scavenger hunt.