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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."

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Book ID Asin: 199957950X
Book Title: The Hundred-Page Machine Learning Book
Book Author: Andriy Burkov
Book Format and Price:
Book Price: unknown
Book Category: Books, Computers & Technology, Computer Science and unknown
Book Rating: 776 ratings

The Hundred-Page Machine Learning Book by Andriy Burkov Book Review

Name: Leonard B.
Rating: 5.0 out of 5 stars
Title: Softwareengineersthisisthemachinelearningbookyouneed
Date: Reviewed in the United States on January 17, 2019
Review: WARNING: It's now possible to buy a counterfeit copy of Andriy's book! To avoid that, click on "See All Buying Options" button and choose *Amazon.com* as the seller.I'm a software engineer currently working for a big tech company. This is hands down the book you need to grok and master machine learning concepts. As a programmer, I have felt capable of utilizing the machine learning tools available, but have felt distant from understanding the many cited academic papers. I can confidently say, just a few chapters into this book, that this is the book I was missing!I have followed Andriy on LinkedIn for a long time now, and always appreciated his posts. When I saw he was publishing a book, I didn't think twice and ordered it. As expected, the book is clear, concise and does a thorough job explaining basic mathematical concepts, machine learning principles, and the most important fundamentals to understand the field.One note: I can tell this book will be useful for a long time. I have many tech related books that become obsolete a few years or even months after they are published. Andriy's approach delves into the core principles, while explaining how to understand further developments into the field. This is something I was missing and truly appreciate.I have a quirk of reading physical books alongside a text-to-speech interface on a digital device. Especially with text books, this is helpful, but not all textbooks are capable of being processed this way. Fortunately, when you purchase the physical book, Andriy sends you the digital edition as well. As a result, I have been able to breeze through the text in my ideal learning state.For those who have been working around the academic machine learning world, but are influenced by it - buy this book! For those who are familiar with machine learning concepts and have gone through all the blog posts and MOOCs you could get your hands on - buy this book!

Name: Y. Yuan
Rating: 1.0 out of 5 stars
Title: Lackinghighqualityoranyworkingexamples
Date: Reviewed in the United States on April 3, 2019
Review: This book has a good coverage of machine learning from many aspects. However, with only a bit over 100 pages, it’s hard to get deep with any subject covered. It lacks high quality or any working examples, which are necessary for reinforcing a reader’s understanding of the math and concepts covered. Good examples also help a reader to quickly ramp up his/her knowledge and skills in whatever learned and apply to solving real problems. You may want to check out another text titled “Machine Learning: a Quantitative Approach,” which provides systematically not only math behind ML but also many working examples implemented with major ML frameworks written in Python, C and C++. In general, it’s better to read and work through one text or at most two with necessary math and solid examples, rather than chase many websites scattered everywhere.The book does offer QR code for a reader to scan with a smart phone and get to the intended websites for more in-depth coverage, but I am not able to use it effectively with my iPhone, since the screen size of a smart phone is not large enough for reading a text book. However, the author is certainly a very capable ML expert and has a great potential to make it significantly better.Finally, the book is too expensive as more like a pocket reference.

Name: Sandro Skansi
Rating: 5.0 out of 5 stars
Title: Excellenthandbookfordatasciencemanagersgreatforalmosteverybodyelse
Date: Reviewed in the United States on February 18, 2019
Review: This is a superbly written book, and a must have for anyone interested in machine learning. In short, this is my review.But let me point a few details. This book, unlike Chollet Deep Learning with Python, or Gulli's Keras or Tensorflow book has not placed emphasis on providing code, but on explaining concepts, and this makes it complementary to those books. But this does not mean that this book is useless for those interested in just coding models--quite the opposite--it delivers exactly what it promises in the title, a great introduction to machine learning, and I think that this makes it the go-to handbook for managers, or software developers looking for a single volume on machine learning. In essence, anyone not interested in having code delivered: either because she is a manager and will not code, or because she is a developer and does not need sample code. For those groups, this is the best book on the market.For the student or researcher, this book is well worth the money, but it is not everything you might need (although you will get a lot, especially considering the length of Andry's book). It is however a great textbook for a general machine learning course (covering not just deep learning as the most popular paradigm right now), and it would be great to have around 10 Jupyter notebooks with code to supplement it.One minor point, but this is just me, I would have loved to see symbolic machine learning (version spaces, inverse resolution) touched upon, but in 100 pages you simply cannot cover everything.

Name: Constantine V.
Rating: 3.0 out of 5 stars
Title: meh
Date: Reviewed in the United States on May 12, 2019
Review: I was looking for a (shorter) introduction to modern ML and when i saw this book is a bestseller decided to buy it.Unfortunately, there is little to learn from it and while it has positive sides, overall i would not recommend buying it.- yes, this book has colored illustrations - but they are not very helpful.- yes, this is book is short. But when you see half of page dedicated to K-NN and Bayes Formula .. how is it possible to explain them in two paragraphs ? It is impossible.Overall, this book feels like a compilation of wikipedia snippets. And print quality is not the best.Get "Pattern Recognition and Machine Learning" (2006) by Christopher Bishop for intro to fundamental concepts. Then wikipedia more recent stuff.