The Hundred-Page Machine Learning Book

Burkov compresses the field into a book you can read in a weekend, with the mathematics present but held to what is genuinely needed. It covers supervised learning from linear and logistic regression through decision trees, support vector machines and nearest neighbours, then neural networks, then the practical matters of feature engineering, regularisation, hyperparameter tuning and evaluation metrics. Later chapters touch unsupervised learning, dimensionality reduction and applied topics such as recommender systems and ranking. The density is deliberate, so it works as a map of the territory rather than as a tutorial. Read it to hold a credible technical conversation with the people who do build models.