Stanford University · on YouTube
The complete recorded lectures of Stanford's graduate ML course, deriving generalized linear models, SVMs, kernels, EM, learning theory, and RL from first principles. Choose this if you want the mathematical depth behind the algorithms, not just how to call them.
Aspiring data scientists and ML engineers. This is an advanced course in Machine Learning, running about 20 lectures, ~1.5 hours each.
Free. Full lecture recordings published by Stanford Online; no assignments graded.
A micro-course that gets you from zero to your first random forest model and Kaggle competition submission in an afternoon. Perfect for people who learn by doing and want immediate, runnable code.
MIT's undergraduate ML course (Fall 2020) with notes, auto-graded exercises, and labs covering perceptrons, gradient descent, neural nets, CNNs, RNNs, and reinforcement learning. Best for learners comfortable with linear algebra who want an MIT-level problem-set experience for free.
Yaser Abu-Mostafa's celebrated course explains why learning works: VC dimension, bias-variance, overfitting, regularization, and validation, alongside core algorithms. Take it if you want the theoretical intuition that most applied courses skip.