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 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."
Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."
Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."
Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''
Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''
Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."
Everything you really need to know in Machine Learning in a hundred pages.
This is the first of its kind "read first, buy later" book. You can find the book online, read it, and then come back to pay for it if you liked the book or found it useful for your work, business or studies.
Not Really For Beginners
While this book is presented as a primer for Machine Learning enthusiast and practitioners ranging from beginner to expert, I would argue against diving headfirst into it if you have not done some pre-work first. To be fair, the book really is well put together and does a great job of explaining and connecting various ML concepts. However, to get the most out of this text I recommend the following build-up process before reading.
First, read the “Machine Learning for Everyone” post on the vas3k blog to make sure this is a topic you are really are interested in, and to demystify yourself of that the idea that all of this ML stuff is synonymous with AI. Second, take a Python course at either codecademy or a similar online self-paced learning website. Then I recommend taking a Linear Algebra course at a site like Kahn academy, especially if it has been many years since your last math course. Lastly, I recommend taking a Data Science course at either General Assembly, Kaggle, or Data Camp to get the basics and generally terminology down. At this point you will likely be a bit overwhelmed and wondering how to form one coherent big picture of all of this stuff you just learned.
It is at this point where the “The Hundred-Page Machine Learning Book” comes in perfectly. It resets all you have learned to this point into one coherent picture. Reading it at this point also will require less look up of the referenced topics, math, and libraries / tools. While the book is only 100 pages and reads fast, it is probably best to chunk it up into no more than a chapter a day and read it over two weeks. It has also has a long shelf life because you will find yourself referencing back to it from time-to-time.