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.
Moving beyond technical implementation, Alpaydin increasingly addresses the broader impact of ML systems. The fourth edition emphasizes the ethical and legal implications of automated decision-making. This includes critical discussions on data privacy , algorithmic , and the growing need for explainability and the growing need for explainability