Andrew Ng's Machine Learning Specialization is the standard first ML course, and it has earned that status. Ng explains each idea — gradient descent, regularization, decision trees, recommender systems — with a patience that makes difficult material feel reasonable, and the updated version uses Python notebooks instead of the old course's Octave. It assumes only high-school math. Take it in order, do the optional labs, and you'll have the vocabulary to understand nearly everything else in this feed.
Real problems and public leaderboards. Read the winners' write-ups after every competition.
The Bayesian classic. Heavy going, but the most complete probabilistic treatment of ML.
Microsoft Research
MLOps done properly: testing, versioning, serving, and monitoring models in production.
Made With ML
Amazon's visual essays on bias-variance, ROC curves, and more. Beautiful and accurate.
Amazon Machine Learning University
A broad tour of classic algorithms with code templates. Light on theory, heavy on doing.
ISL's demanding older sibling. The reference for the mathematics of classical ML.
The clearest book on the statistics behind ML, now with Python labs. Still free.
James, Witten, Hastie, Tibshirani & Taylor
MIT's rigorous course with demanding projects. Part of the Statistics and Data Science MicroMasters.
MIT Learn·~15 weeks
Géron's practical book is the best bridge from tutorials to real projects. Keep it on your desk.
Unusually good documentation that doubles as a textbook on classical ML methods.
scikit-learn
The mathematical version of Ng's course. Its lecture notes are among the best written on ML.
Imperial College's linear algebra, calculus, and PCA taught for ML. Fills the gaps most self-learners have.
Hands-on notebooks with real data in a few hours. Then enter a beginner competition.
Friendly, precise explanations of each algorithm. Watch the relevant video whenever a concept feels fuzzy.
Google's fast, practical introduction with interactive visualizations. A solid overview in a weekend.
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