Covers nearly all classical ML: supervised (regression, classification, SVMs, trees), unsupervised (clustering, PCA, EM), ensemble methods, and introductory deep learning. The organization is logical — each chapter builds on the last.

: Expanded material now includes deep reinforcement learning and policy gradient methods .

4.5/5 stars

Alpaydin, a professor at Boğaziçi University, masterfully bridges the gap between:

: It is described as "dry" and technical, making it less suitable for casual readers or those without a solid background in calculus and probability.