PDF Download The Hundred-Page Machine Learning Book
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The Hundred-Page Machine Learning Book
PDF Download The Hundred-Page Machine Learning Book
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Review
"This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program."--Deepak Agarwal, VP of Artificial Intelligence at LinkedIn"This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field."--Karolis Urbonas, Head of Data Science at Amazon"I wish such a book existed when I was a statistics graduate student trying to learn about machine learning. There is the right amount of math which demystify the centerpiece of an algorithm with succinct but very clear descriptions. I'm also impressed by the widespread coverage and good choices of important methods as an introductory book (not all machine learning books mention things like learning to rank or metric learning). Highly recommended to STEM major students."--Chao Han, VP, Head of R&D at Lucidworks"This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program."--Sujeet Varakhedi, Head of Engineering at eBay"The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning. In his book, Andriy Burkov distills the ubiquitous material on Machine Learning into concise and well-balanced intuitive, theoretical and practical elements that bring beginners, managers, and practitioners many life hacks."--Vincent Pollet, Head of Research at Nuance
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From the Author
This long-awaited day has finally come and I'm proud and happy to announce that The Hundred-Page Machine Learning Book is now available to order in a high-quality color paperback edition as well as a Kindle edition.For three months, I worked hard to write a book that will make a difference. I firmly believe that I succeeded.I'm so sure about that because I received dozens of positive feedback. Both from readers who just start in artificial intelligence and from respected industry leaders. I'm extremely proud that such best-selling AI book authors and talented scientists as Peter Norvig and Aurélien Géron endorsed my book and wrote the texts for its back cover and that Gareth James wrote the Foreword.This book wouldn't be of such high quality without the help of volunteering readers who sent me hundreds of text improvement suggestions. The names of all volunteers can be found in the Acknowledgments section of the book.
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Product details
Paperback: 158 pages
Publisher: Andriy Burkov (January 13, 2019)
Language: English
ISBN-10: 199957950X
ISBN-13: 978-1999579500
Product Dimensions:
7.5 x 0.4 x 9.2 inches
Shipping Weight: 8.8 ounces (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
25 customer reviews
Amazon Best Sellers Rank:
#1,471 in Books (See Top 100 in Books)
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!
The "Hundred-Page Machine Learning Book" by Andriy Burkov, is in my opinion the best book for those working with machine learning libraries but don't have an understanding of the underlying science behind the libraries. I am a machine learning scientist/ engineer and often get asked what is the difference between what I do and what someone that just applies libraries are. This book explains it in a very down to earth way. Yes in this book there is some math used, nothing too excessive and should be easy for anyone with some mathematical experience to grasp.The best part of the book I have to say is that it gives the introduction that I think so many need in understanding that a simple ML library and a coder are not going to present the answers to the questions being asked. If there is a simple question with a clean dataset, then yes someone with some tech knowledge will be able to grab a ML library and come to a conclusion. For a more in-depth question with a messy dataset or when the basic libraries don't cover the problem someone with more in-depth knowledge of the science is needed. Thank you Andriy Burkov for writing this amazing book.
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.
A great book for breaking into machine learning or just looking up material. Burkov seems to be an experienced ML-engineer, which allows him to recommend ways of approaching certain problems. He also explains typical ways of solving known problems, like transfer learning, handling imbalanced datasets, and combining models. With the addition of some introduction to newer and less known concepts, this book kicks you in the right direction.If you're unsure whether to read this book; it's only a hundred pages - read it.
This book achieves what no other book does on this topic - concise and lucid explanations of an impressively broad set of machine learning topics in less than 150 pages. This book is an absolute must-have for anyone interested in machine learning, from the novice to expert. As a practicing machine learning engineer, I have read many different books on the topic and this is by far the most valuable. You will not be disappointed!
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