Machine Learning
How machines learn from data: regression, trees, clustering, evaluation and the classic algorithms.
Expert
Research-level depth and specialist material.
Pattern Recognition and Machine Learning
Christopher Bishop's graduate textbook on Bayesian methods in machine learning, made free by Microsoft Research.
Christopher M. Bishop (Microsoft Research)
Probabilistic Machine Learning: An Introduction
Kevin Murphy's comprehensive book on ML from a probabilistic point of view; the draft is free online.
Kevin P. Murphy (MIT Press)
scikit-learn User Guide
The official guide to every scikit-learn estimator, with the maths, practical tips and runnable examples.
scikit-learn developers
Stanford CS229: Machine Learning (Andrew Ng, Autumn 2018)
The full graduate-level Stanford lecture series on the mathematics of supervised, unsupervised and reinforcement learning. Opens on YouTube; Stanford does not allow it to play inside other sites.
Stanford Online
The Elements of Statistical Learning
The classic advanced text on statistical learning, free as a PDF from the authors.
Hastie, Tibshirani and Friedman