The Hundred-Page Machine Learning Book (The Hundred-Page Books)

If you are serious about learning machine learning, The Hundred-Page Machine Learning Book by Andriy Burkov is the first book you should pick up. It’s nothing short of a triumph — clear, concise, brilliantly organized, and incredibly practical. In a little over a hundred pages, Burkov manages to explain complex concepts like supervised and unsupervised learning, model evaluation, neural networks, and even advanced topics like ensemble methods and dimensionality reduction. Every chapter is filled with wisdom distilled from years of real-world experience, written in a style that is both accessible to beginners and valuable for seasoned professionals. What impressed me the most is how efficient the book is: there’s not a single wasted word. Every paragraph adds real value. The balance between mathematical intuition, algorithmic depth, and practical advice is absolutely perfect. Plus, the references to additional resources (via QR codes and links) make this book a living gateway to even deeper learning. If you’ve ever been intimidated by machine learning, this book will completely change that. It makes the field feel exciting, achievable, and even fun. I cannot recommend it highly enough. 5/5 stars — This book belongs on the desk of every aspiring data scientist, engineer, or AI enthusiast!

Disclaimer: The author asked me to review this book, though I had already purchased my own copy. I promise to be 100% honest in how I feel about this book, both the good and the less so. Overview: This book does exactly what it states. It's a 100+ page book that gives you an overview of machine learning, the math behind most of the reviewed techniques so you can follow along with current research to an extent), and QR code links to further reading. The author also follows a 'read first, buy later' policy, which I respect. The book is very well organized, giving the reader an introduction and discussion on the mathematical notation used, a well written chapter that discusses several very common algorithms, talks about best practices (like feature engineering, breaking up the data into multiple sets, and tuning the model's hyperparameters), digs deeper into supervised learning, discusses unsupervised learning, and gives you a taste of a variety of other related topics. What I Like: This is a well rounded book, far more so than most books I've read on machine learning or artificial intelligence. After reading through this, I feel like I can competently discuss the subject, read one of the simpler machine learning research papers, and not be totally lost on the mathematics involved. The language used is concise and reads very well, showing very tight editing. What I Didn't Care For: I know that this is a general introduction and meant to be kept short. Like many other reviewers, however, I would have enjoyed a deeper look into everything that was in this book. What I Would Like To See: I know that the author is currently writing a data engineering book without the 100 page limitation. Personally, I would like to see him write a ML Math book (I'm weird like that) as well as an MLOps book. I expect the later to be what he writes next, if he chooses to continue writing. Overall, I got a LOT out of this book and look forward to more. I am giving a rating of 4.8 out of 5. If I include the wiki and further reading, I would bump it up to 4.9.

I've gone through different books, papers and courses on ML and Artificial intelligence, and I've found that a majority of them are either overwhelmingly dense, or disappointingly shallow, with very few of them hitting any sort of sweet spot. This book is one of the few exceptions. Despite being short, it manages to cover a lot of ground without sacrificing a fair treatment of the basics. There's an exceptionally good balance between math and concepts in my opinion, and it's all explained in very simple terms, without ever feeling pretentious or cryptic. I think the author did an outstanding job distilling a great deal of useful information into 100-ish pages while avoiding making this a dense read. It's actually the only book I've been able to breeze through while still getting a lot of useful insights in the process. In fact, even though I already had some experience on the field when I read this book, I found that the way some concepts and topics are presented provided me with a new way of approaching them or thinking about them that further deepened my understanding of those topics, and allowed me to explore them in ways I had not done before. I wish this book existed when I started learning ML. It would have made a lot of thing clearer from the start. All in all; This book is a fantastic resource that serves as a perfect introduction to the topic for beginners, and a good "refresher" and a source of invaluable tips and insights for the more experienced ML practitioners.

Really well written and concise but some chapters are a little confusing when topics are mentioned but not explained until later in the book. Overall a good read if you want to get a sense of what ML is about. Lots of math and probably not the place to start if you haven't done stats or calc in a few decades.

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!

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.

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

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!

If I was going to make a list of essential books in this domain it would include Deep Learning (Goodfellow et alia), AIMA (Norvig et alia), ISL/ESL (James et alia), and then work through Fast.ai on the side to get your hands dirty. Now here's the newcomer, highly recommended by the other authors in the above list: The Hundred-Page Machine Learning Book (Burkov) - basic math refresher and overview of the field, brilliant and new. Burkov has a growing interactive website and community, is actively on reddit doing AMAs, and is continuously allowing his source material to evolve, as it should in this field! Top notch resource. He links to more advanced resources in the different topics he introduces for the student who wishes to excel. I have a degree in mathematics, and I recommend this book to interested readers with any level of prior knowledge.