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The Hundred-Page Machine Learning Book

Description:

WARNING! To avoid counterfeit, make sure that the book ships from and sold by Amazon. Avoid third-party sellers.

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.''

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.

Read more

Review

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-hat 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."

Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "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."

Karolis Urbonas, Head of Data Science at Amazon: "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."

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. 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."

Sujeet Varakhedi, Head of Engineering at eBay: "Whether you want to become a machine learning practitioner or looking for an everyday resource, Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page. It manages to structure all the important concepts from foundations to applications into a relatively quick read and leave the reader engaged at all times."

Vincent Pollet, Head of Research at Nuance: "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."

About the Author

Andriy Burkov is the author of "The Hundred-Page Machine Learning Book" and "Machine Learning Engineering," both of which became #1 Best Sellers on Amazon. He holds a Ph.D. in Artificial Intelligence and is a recognized expert in machine learning and natural language processing.As a machine learning expert and leader, Andriy has successfully led dozens of production-grade AI projects in different business domains at Fujitsu and Gartner. His previous books have been translated into more than a dozen languages and are used as textbooks in many universities worldwide. His work has impacted millions of machine learning practitioners and researchers worldwide.Currently, Andriy is the Head of Machine Learning at TalentNeuron, where he develops AI solutions for talent marketplace analytics. He uses language models and other machine learning tools to analyze billions of job postings across 30+ languages in near real time.

Reviews:

5.0 out of 5 stars I admire what the author achieved here

H. · 27 October 2023

The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here.After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function).Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present.The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams.That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning).In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least.To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this.

5.0 out of 5 stars Learn the background behind the methods

C.S. · 11 May 2025

This is not the book you get for sample code and immediate applications, but it is a fantastic resource to learn more of the theory behind machine learning methods. You will improve your use of models by learning the background in this book.

4.0 out of 5 stars too expensive but has some essential parts

j. · 29 December 2019

This books price is a shame. Aside from that the content is good for the most part. Sadly it doesnt explain back propagation which would have been nice and theres no gaussian section which seemed odd. The best part about this book for me is its one of the few that actually explains the notation properly. I find that this subject appears a lot more difficult because of the dense notation which many books go out of their way not to define. This one does a good job of making sure you understand what all the letters and subscripts mean, and for that I was very happy

5.0 out of 5 stars Just enough pages

A. · 15 February 2025

The book is extremely comprehensive with the knowledge, but it's more than enough to know the basics, better take this one, than much longer but empty in context books.

5.0 out of 5 stars Excellent: brief but in-depth introduction

a. · 29 January 2024

This is an excellent brief but in-depth introduction to the subject for complete beginners who have a mathematical background. In the first 6 pages it explains from very basic principles to producing a complete machine learning model using one technique. It then explains other techniques, including multi-level neural networks. It is a remarkably easy read considering the level of detail it goes into. I found it an excellent first book on the subject.

5.0 out of 5 stars Amazing book

H. · 7 February 2025

This book is one of the best books I have read on machine learning. It’s beautifully written with concise and clear explanations. The author does an amazing job in only communicating the necessary on such a broad and deep project. I got the hard copy and it’s a pleasure to have. Thank you

5.0 out of 5 stars All that you need in 136 pages!

S.C. · 6 April 2020

Difficult to believe but this book describes a variety of machine learning concepts and algorithms in just 136 pages. Of course it lacks of applied machine learning paradigms but there are plenty of books out there to improve your practical skills - e.g. Hands On Machine Learning with Scikit-Learn, Keras & Tensorflow. If you are a beginner on the field this book looks challenging but after you grasp the key concepts you will know how thinks work! On the other hand experienced data scientist and machine learning engineers can refresh their knowledge or even self-improve. Lastly, I really enjoyed QR codes which provide additional material which is constantly up to date.

5.0 out of 5 stars Straight to the point

D.P.D. · 13 November 2021

I've bought a few books lately on machine learning, some with bigger price tags, more pages and a lot less information but with grand titles about "Artificial Intelligence". I learned more about machine learning in pages 1 to 5 than I have in two dozen in Russell & Norvig.The author does an excellent job with a difficult subject. He even explains the mathematical notation in chapter 2 that will bring a great deal of clarity to those who have neither studied mathematics, statistic or computer science - like me. The world needs more books like this.

Excellent book, for work, science and curiosity

B.M. · 1 June 2024

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!

Una muy buena introducción al tema

I.G.C. · 10 March 2021

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.

Loved this book

N.W. · 10 December 2019

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

Wonderful short book that provides a backbone structure for your machine learning journey

K. · 30 December 2021

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!

One of a small handful of essential ML books.

B. · 27 February 2019

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.

Visit the Andriy Burkov Store

The Hundred-Page Machine Learning Book

Product ID: K199957950K
Condition: New

4.6

AED28090

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Visit the Andriy Burkov Store

The Hundred-Page Machine Learning Book

Product ID: K199957950K
Condition: New

4.6

The Hundred-Page Machine Learning Book-0
Type: Paperback

AED28090

Price includes VAT & Import Duties
Availability: In Stock

Quantity:

|

Order today to get by 7-14 business days

This item qualifies for free delivery

Returns & Warranty policies

Imported From: United Kingdom

At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.

BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.

Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.

If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.

Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.

All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.

While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.

Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.

BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.

All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.

All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.

Description:

WARNING! To avoid counterfeit, make sure that the book ships from and sold by Amazon. Avoid third-party sellers.

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.''

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.

Read more

Review

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-hat 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."

Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "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."

Karolis Urbonas, Head of Data Science at Amazon: "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."

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. 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."

Sujeet Varakhedi, Head of Engineering at eBay: "Whether you want to become a machine learning practitioner or looking for an everyday resource, Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page. It manages to structure all the important concepts from foundations to applications into a relatively quick read and leave the reader engaged at all times."

Vincent Pollet, Head of Research at Nuance: "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."

About the Author

Andriy Burkov is the author of "The Hundred-Page Machine Learning Book" and "Machine Learning Engineering," both of which became #1 Best Sellers on Amazon. He holds a Ph.D. in Artificial Intelligence and is a recognized expert in machine learning and natural language processing.As a machine learning expert and leader, Andriy has successfully led dozens of production-grade AI projects in different business domains at Fujitsu and Gartner. His previous books have been translated into more than a dozen languages and are used as textbooks in many universities worldwide. His work has impacted millions of machine learning practitioners and researchers worldwide.Currently, Andriy is the Head of Machine Learning at TalentNeuron, where he develops AI solutions for talent marketplace analytics. He uses language models and other machine learning tools to analyze billions of job postings across 30+ languages in near real time.

Reviews:

5.0 out of 5 stars I admire what the author achieved here

H. · 27 October 2023

The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here.After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function).Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present.The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams.That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning).In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least.To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this.

5.0 out of 5 stars Learn the background behind the methods

C.S. · 11 May 2025

This is not the book you get for sample code and immediate applications, but it is a fantastic resource to learn more of the theory behind machine learning methods. You will improve your use of models by learning the background in this book.

4.0 out of 5 stars too expensive but has some essential parts

j. · 29 December 2019

This books price is a shame. Aside from that the content is good for the most part. Sadly it doesnt explain back propagation which would have been nice and theres no gaussian section which seemed odd. The best part about this book for me is its one of the few that actually explains the notation properly. I find that this subject appears a lot more difficult because of the dense notation which many books go out of their way not to define. This one does a good job of making sure you understand what all the letters and subscripts mean, and for that I was very happy

5.0 out of 5 stars Just enough pages

A. · 15 February 2025

The book is extremely comprehensive with the knowledge, but it's more than enough to know the basics, better take this one, than much longer but empty in context books.

5.0 out of 5 stars Excellent: brief but in-depth introduction

a. · 29 January 2024

This is an excellent brief but in-depth introduction to the subject for complete beginners who have a mathematical background. In the first 6 pages it explains from very basic principles to producing a complete machine learning model using one technique. It then explains other techniques, including multi-level neural networks. It is a remarkably easy read considering the level of detail it goes into. I found it an excellent first book on the subject.

5.0 out of 5 stars Amazing book

H. · 7 February 2025

This book is one of the best books I have read on machine learning. It’s beautifully written with concise and clear explanations. The author does an amazing job in only communicating the necessary on such a broad and deep project. I got the hard copy and it’s a pleasure to have. Thank you

5.0 out of 5 stars All that you need in 136 pages!

S.C. · 6 April 2020

Difficult to believe but this book describes a variety of machine learning concepts and algorithms in just 136 pages. Of course it lacks of applied machine learning paradigms but there are plenty of books out there to improve your practical skills - e.g. Hands On Machine Learning with Scikit-Learn, Keras & Tensorflow. If you are a beginner on the field this book looks challenging but after you grasp the key concepts you will know how thinks work! On the other hand experienced data scientist and machine learning engineers can refresh their knowledge or even self-improve. Lastly, I really enjoyed QR codes which provide additional material which is constantly up to date.

5.0 out of 5 stars Straight to the point

D.P.D. · 13 November 2021

I've bought a few books lately on machine learning, some with bigger price tags, more pages and a lot less information but with grand titles about "Artificial Intelligence". I learned more about machine learning in pages 1 to 5 than I have in two dozen in Russell & Norvig.The author does an excellent job with a difficult subject. He even explains the mathematical notation in chapter 2 that will bring a great deal of clarity to those who have neither studied mathematics, statistic or computer science - like me. The world needs more books like this.

Excellent book, for work, science and curiosity

B.M. · 1 June 2024

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!

Una muy buena introducción al tema

I.G.C. · 10 March 2021

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.

Loved this book

N.W. · 10 December 2019

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

Wonderful short book that provides a backbone structure for your machine learning journey

K. · 30 December 2021

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!

One of a small handful of essential ML books.

B. · 27 February 2019

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.

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Similar items from “AI & Machine Learning”

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https://www.bolo.ae/products/K199957950K