The Hundred-Page Machine Learning Book

Master machine learning through clarity, not complexity―in a book engineered to teach with exceptional conciseness.

Translated into 11 languages and used in thousands of universities worldwide, this book takes a unique approach: it assumes that your time is valuable. Instead of drowning you in theory or skimming the surface, it delivers a complete education in modern machine learning, focusing on what matters in practice. From fundamental algorithms that form the backbone of many applications, to cutting-edge deep learning and neural networks, you'll understand how these tools work and how to use them.

What sets this book apart is its careful progression through key concepts. You'll start with essential mathematical concepts and gradually progress through the most practically important machine learning algorithms. You'll learn practical skills like feature engineering, regularization, handling imbalanced datasets, ensembles, and model evaluation that help turn theory into working systems.

The book covers not just supervised learning, but also clustering, topic modeling, metric learning, learning to rank, and recommendation systems, giving you a complete toolkit for solving modern machine learning challenges.

This isn't just another theoretical textbook. Every chapter reflects the author's real-world experience, focusing on techniques that work in practice. Whether you're building a recommendation system, analyzing customer data, or working with images and text, you'll find practical guidance here.

This isn't a high-level overview either. The book explores each concept with precisely the right level of technical detail—enough to create those crucial “a-ha!” moments of understanding, but not so much that you get overwhelmed by mathematical notation or theoretical abstractions. It hits that sweet spot where complex ideas click into place naturally, making it valuable for both newcomers looking to build a strong foundation and experienced practitioners seeking to expand their toolkit.

What's Inside

• Supervised and unsupervised learning algorithms, including deep neural networks
• Clear, intuitive explanations of algorithms and mathematics that preserve essential details
• Practical techniques for building, debugging, and evaluating models
• Advanced topics including ensembles, recommender systems, and metric learning