Download The HundredPage Machine Learning Book eBook Andriy Burkov
Download The HundredPage Machine Learning Book eBook Andriy Burkov
Download As PDF : The HundredPage Machine Learning Book eBook Andriy Burkov
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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."
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 "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.''
Vincent Pollet, Head of Research at Nuance "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''
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.
Download The HundredPage Machine Learning Book eBook Andriy Burkov
"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."
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The HundredPage Machine Learning Book eBook Andriy Burkov Reviews :
The HundredPage Machine Learning Book eBook Andriy Burkov Reviews
- 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! - 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. - 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. - 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. - The Hundred-Page Machine Learning Book is an excellent way to learn the big ideas and key algorithms and models of modern machine learning without spending a huge amount of time. I bought a hard copy because I knew I'd be filling it up with notes, and I did. In addition to the theory, Andriy does a nice job of covering which algorithms are practical or impractical in what kinds of situations.
Despite the book's short length I know that it will also work well as a reference book. If you read the book now and one of the algorithms comes up 8 months from now where you work, you will definitely come back to this book to review. (One note for readers without a strong math background I suggest that you read the "Notation" section of chapter 2--maybe all of chapter 2--before you start reading chapter 1.)
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