CS229: Machine Learning

CS229 is Andrew Ng's graduate-level Stanford ML course — the academic original that preceded his Coursera courses and goes significantly deeper into the mathematical foundations. Where the Coursera ML Specialization builds intuition and implements in Python, CS229 works through full probabilistic derivations and proofs: maximum likelihood estimation for supervised learning, EM algorithm for unsupervised learning, support vector machines and kernels, principal component analysis, reinforcement learning with MDPs and Q-learning, and the theoretical foundations of learning guarantees.

The 2018 lecture recordings by Andrew Ng are freely available and widely considered among the most comprehensive introductions to ML theory available anywhere. For learners who want to read ML papers, work in research, or understand why ML methods work rather than just how to apply them, CS229 is the reference.