The Hundred-Page Machine Learning Book (The Hundred-Page Books)
Product ID: 415435842
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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 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 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. Endorsements Peter Norvig , Research Director at Google , co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field." Aurélien Géron , Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field." More endorsements on themlbook.com Review: I admire what the author achieved here - The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here. After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function). Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present. The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams. That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning). In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least. To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this. Review: Amazing book - This book is one of the best books I have read on machine learning. It’s beautifully written with concise and clear explanations. The author does an amazing job in only communicating the necessary on such a broad and deep project. I got the hard copy and it’s a pleasure to have. Thank you
| Best Sellers Rank | 800,370 in Books ( See Top 100 in Books ) 727 in Computer Science (Books) |
| Customer Reviews | 4.6 out of 5 stars 1,254 Reviews |
H**.
I admire what the author achieved here
The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here. After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function). Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present. The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams. That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning). In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least. To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this.
H**D
Amazing book
This book is one of the best books I have read on machine learning. It’s beautifully written with concise and clear explanations. The author does an amazing job in only communicating the necessary on such a broad and deep project. I got the hard copy and it’s a pleasure to have. Thank you
J**O
too expensive but has some essential parts
This books price is a shame. Aside from that the content is good for the most part. Sadly it doesnt explain back propagation which would have been nice and theres no gaussian section which seemed odd. The best part about this book for me is its one of the few that actually explains the notation properly. I find that this subject appears a lot more difficult because of the dense notation which many books go out of their way not to define. This one does a good job of making sure you understand what all the letters and subscripts mean, and for that I was very happy
C**T
Learn the background behind the methods
This is not the book you get for sample code and immediate applications, but it is a fantastic resource to learn more of the theory behind machine learning methods. You will improve your use of models by learning the background in this book.
A**H
Excellent: brief but in-depth introduction
This is an excellent brief but in-depth introduction to the subject for complete beginners who have a mathematical background. In the first 6 pages it explains from very basic principles to producing a complete machine learning model using one technique. It then explains other techniques, including multi-level neural networks. It is a remarkably easy read considering the level of detail it goes into. I found it an excellent first book on the subject.
L**O
ML is a lot of fun
This book is all pure dust gold, it will help you to understand everything you need to know about ML
M**G
Short and concise
For the most part, I liked the short and concise explanations. They were so concise I found my self reading and rereading sentences simply because there was so much information condensed into them. I disliked the treatment of backpropagation, which was almost non-existent and the explanation of convolutional neural networks was difficult to follow -despite the fact that I know how these networks work. Overall I feel that this is a good book to read if you have already had a healthy introduction to machine learning from other sources but there is no getting away from the fact that it is a little too short. The price also is a little high for such a slim book. There are some dreadful books about machine learning doing the rounds at the moment. This book is not one of them.
A**T
Just enough pages
The book is extremely comprehensive with the knowledge, but it's more than enough to know the basics, better take this one, than much longer but empty in context books.
B**E
Excellent book, for work, science and curiosity
I am a materials engineer and this book helped me a lot to quickly understand the concepts of machine learning with a very basic knowledge. I am very grateful to have come across this book. While I was working on my Master's thesis on a topic related to computer vision, the book was very accessible thanks to its clear explanations and helped me to quickly get into my topic. It also proved to be directly applicable to my professional work. I would recommend this book to anyone who wants to learn more about machine learning and also to professionals in the field who want a reference book. Thank you Andriy for this great book!
I**L
Una muy buena introducción al tema
Es uno de los mejores libros que he visto a nivel principiante. Es importante que el objetivo del libro no es que tengas horas experiencia práctica al terminar de leerlo, sino dar un "panorama general" del Machine Learning, cosa que el autor hace de forma magistral.
N**S
Loved this book
So succinct and doesn't skip the math on anything. An intro to ML but has something for everyone to learn. Great to keep on the shelf at home or work for reference
K**O
Wonderful short book that provides a backbone structure for your machine learning journey
I'd say no one book or course is adequate for mastering Machine Learning, but this book is really helpful! It may not cover all aspects in great detail, but it does touch all the important points and with admirable clarity. The book is like a structured learning guide, based on which we can get a baseline understanding, and then go elsewhere to pick up more details as needed. I use it in conjunction with half a dozen other machine learning books and online courses. I love this book!
E**O
ottima introduzione al machine learning, utile come riferimento anche per chi è più esperto
Volevo una buona ma veloce introduzione al mondo del Machine Learning, per cominciare ad applicarlo al mio lavoro quotidiano. Credo che questo libro sia perfetto per questo scopo. È veramente sintetico ma accurato. Vengono presentate principalmente le tecniche "classiche" di Machine Learning, quelle più importanti e utilizzate, insieme a una serie di "best practices" di riconosciuto successo. Gli algoritmi più moderni, che vengono sviluppati di anno in anno, non sono presenti, ma troverete una buona collezione di algoritmi fondamentali, che vi permetterà anche di comprendere gli sviluppi più recenti di questo settore. Scrivo questa relazione circa un anno dopo l'acquisto del libro. Sto utilizzando il Machine Learning abbastanza spesso nel mio lavoro (anche se non mi ritengo un professionista), e utilizzo ancora questo libro come riferimento, per rinfrescare una formula o un concetto.
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