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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
About the Reader
The book assumes a basic foundation in college-level mathematics. However, it's entirely self-contained, introducing all necessary mathematical concepts through intuitive explanations. This approach ensures that readers with basic mathematical knowledge can follow along without getting lost in complex equations.
Endorsed by Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world, Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, and other industry leaders.
Read endorsements on themlbook.com
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
About the Reader
The book assumes a basic foundation in college-level mathematics. However, it's entirely self-contained, introducing all necessary mathematical concepts through intuitive explanations. This approach ensures that readers with basic mathematical knowledge can follow along without getting lost in complex equations.
Endorsed by Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world, Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, and other industry leaders.
Read endorsements on themlbook.com
From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.
If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.
-Cassie Kozyrkov, Chief Decision Scientist at Google
Foundational work about the reality of building machine learning models in production.
-Karolis Urbonas, Head of Machine Learning and Science at Amazon
The most comprehensive book on the engineering aspects of building reliable AI systems.
If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.
-Cassie Kozyrkov, Chief Decision Scientist at Google
Foundational work about the reality of building machine learning models in production.
-Karolis Urbonas, Head of Machine Learning and Science at Amazon
Master language models through mathematics, illustrations, and code―and build your own from scratch!
The Hundred-Page Language Models Book by Andriy Burkov, the follow-up to his bestselling The Hundred-Page Machine Learning Book (now in 12 languages), offers a concise yet thorough journey from language modeling fundamentals to the cutting edge of modern Large Language Models (LLMs). Within Andriy's famous hundred-page format, readers will master both theoretical concepts and practical implementations, making it an invaluable resource for developers, data scientists, and machine learning engineers.
The Hundred-Page Language Models Book allows you to:
- Master the mathematical foundations of modern machine learning and neural networks
- Build and train three architectures of language models in Python
- Understand and code a Transformer language model from scratch in PyTorch
- Work with LLMs, including instruction finetuning and prompt engineering
Written in a hands-on style with working Python code examples, this book progressively builds your understanding from basic machine learning concepts to advanced language model architectures. All code examples run on Google Colab, making it accessible to anyone with a modern laptop.
Endorsements
Vint Cerf, Internet pioneer and Turing Award recipient: This book cleared up a lot of conceptual confusion for me about how Machine Learning actually works - it is a gem of clarity.
Tomás Mikolov, the author of word2vec and FastText: The book is a good start for anyone new to language modeling who aspires to improve on state of the art.
Master language models through mathematics, illustrations, and code―and build your own from scratch!
The Hundred-Page Language Models Book by Andriy Burkov, the follow-up to his bestselling The Hundred-Page Machine Learning Book (now in 12 languages), offers a concise yet thorough journey from language modeling fundamentals to the cutting edge of modern Large Language Models (LLMs). Within Andriy's famous hundred-page format, readers will master both theoretical concepts and practical implementations, making it an invaluable resource for developers, data scientists, and machine learning engineers.
The Hundred-Page Language Models Book allows you to:
- Master the mathematical foundations of modern machine learning and neural networks
- Build and train three architectures of language models in Python
- Understand and code a Transformer language model from scratch in PyTorch
- Work with LLMs, including instruction finetuning and prompt engineering
Written in a hands-on style with working Python code examples, this book progressively builds your understanding from basic machine learning concepts to advanced language model architectures. All code examples run on Google Colab, making it accessible to anyone with a modern laptop.
Endorsements
Vint Cerf, Internet pioneer and Turing Award recipient: This book cleared up a lot of conceptual confusion for me about how Machine Learning actually works - it is a gem of clarity.
Tomás Mikolov, the author of word2vec and FastText: The book is a good start for anyone new to language modeling who aspires to improve on state of the art.