Mathematics for Machine Learning

9 reviews for Mathematics for Machine Learning

1. Andrea Rotoloni

   Rated 5 out of 5

   July 7, 2023

   5.0 out of 5 stars è stato consegnato

   arrived

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   Amazon Customer

   Rated 5 out of 5

   September 3, 2022

   5.0 out of 5 stars Incredible Resource

   I had been looking for a book to bridge the gap between implementing machine learning code on the granular level and understanding it from a theoretic...More

   I had been looking for a book to bridge the gap between implementing machine learning code on the granular level and understanding it from a theoretical perspective and the search wasn't going well. Lots of other books that I tried before finding this one promised to help programmers become better mathematicians (or at least show them the math they need to learn in order to achieve that goal) but would almost always just provide code without context, or run through some incredibly basic, introductory level math without explaining at all how it connects to the various machine learning algorithms you'll be implementing as a programmer.
   
   This book, however, takes the math seriously, and is incredibly direct and efficient in the introduction of new, relevant topics in calculus, linear algebra, and probability and statistics that you'll need to know if you want to truly understand the libraries you're using. I find myself reading a section in the book, going back to a "dedicated" textbook on the subject at hand - linear algebra or calculus or probability and statistics - and further studying the material, and then going back to Mathematics for Machine Learning to make sure I understand the topic better. This is the exact learning flow that I wanted, and the book delivers. Can't recommend enough!

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   Sodipo Gideon Olawale

   Rated 5 out of 5

   March 26, 2022

   5.0 out of 5 stars Review on mathematics for machine learning

   This book really provides the mathematical foundation of machine learning and also points the reader to relevant text and materials for full reference...More

   This book really provides the mathematical foundation of machine learning and also points the reader to relevant text and materials for full reference.

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   Ayshen

   Rated 5 out of 5

   February 27, 2022

   5.0 out of 5 stars Awesome book but it can scare the beginners :)

   Great book who wants to understand the maths behind the ML models, but some parts are rocket science :). I would definitely recommend this book to peo...More

   Great book who wants to understand the maths behind the ML models, but some parts are rocket science :). I would definitely recommend this book to people in academia or to whom who has enough time to dive deep into theoretical aspect of the ML.

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   Tuan Tran

   Rated 5 out of 5

   November 11, 2021

   5.0 out of 5 stars Excellent book for reviewing math materials

   This book is excellent for brushing up your mathematics knowledge required for ML. It is very concise while still providing enough details to help rea...More

   This book is excellent for brushing up your mathematics knowledge required for ML. It is very concise while still providing enough details to help readers determine important parts. This is my go-to if I need to review some concepts or brush up on my knowledge in general.
   
   I wouldn't recommend this book if you have absolutely no prior math experience though as it can be hard to digest and sometimes they would skip parts here and there in proofs and examples. Especially for the probability section, the concepts will be very hard to grasp without prior knowledge

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   JRVV

   Rated 5 out of 5

   February 22, 2021

   5.0 out of 5 stars Great Reference and Refresher Book

   Fits my needs pretty well. A well-curated collection of the essential math for AI and ML. I purchased the physical copy, despite having the free PDF...More

   Fits my needs pretty well. A well-curated collection of the essential math for AI and ML. I purchased the physical copy, despite having the free PDF, because I enjoyed it a lot and plan to re-read it with more detailed note-taking and highlighting on the book itself. I did have to consult other sources just to clear up some parts but was expected in reading a math book. Highly recommended!

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   Peter Washington

   Rated 5 out of 5

   November 22, 2020

   5.0 out of 5 stars This is how math should be taught to CS majors

   In college, I was bored out of my mind during Linear Algebra, Multivariable Calculus, and Statistics courses. I wish the concepts would be introduced ...More

   In college, I was bored out of my mind during Linear Algebra, Multivariable Calculus, and Statistics courses. I wish the concepts would be introduced in the way they are in this book. For example, partial differentiation and gradients are explained in terms of neural network weight optimization / gradient descent. This book is especially valuable if you know the basic intuition behind machine learning and neural networks, and also have a basic intuition behind the math, and want to combine this intuition with a formal mathematical understanding.

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   Mavichov(gh.liang)

   Rated 5 out of 5

   September 13, 2020

   5.0 out of 5 stars Great book for beginners!

   Even though I can get a free e-copy, I still like the paperback version because I flip through it occasionally. This book sketches a clear big picture...More

   Even though I can get a free e-copy, I still like the paperback version because I flip through it occasionally. This book sketches a clear big picture of the knowledge tree for ML and provides necessary build blocks to help you build solid foundations in preparation for practical ML.
   
   You have to be aware this paperback version doesn't come with solutions. One of my reason to buy this is for the solutions. It turned out that only instructors can request solutions from the press company.

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   Estefano Palacios Topic

   Rated 5 out of 5

   April 29, 2020

   5.0 out of 5 stars Brilliant and Precise

   The book is the missing piece between books like Artificial Intelligence: A Modern Approach and the mathematics you require to take such an undertakin...More

   The book is the missing piece between books like Artificial Intelligence: A Modern Approach and the mathematics you require to take such an undertaking. The authors do assume very little prior knowledge from the reader, but it t is recommended that you've had exposure to some of the mathematical topics prior to reading the book. But don't let that stop you if you're a beginner: you'll have to make a few detours to grasp some terms and such. Having said that, a course on single variable calculus ought to be under your belt. That's basically the only prerequisite.
   
   The explanations are clear, and the book is designed to bring clarity and lucidity onto the topics, not send the student on an endless pit of proofs and rigor.

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