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Mathematics for Machine Learning

4.35  ·  Rating details ·  48 ratings  ·  10 reviews
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. ...more
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Published February 20th 2020 by Cambridge University Press
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Average rating 4.35  · 
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محمد
Dec 09, 2020 rated it it was amazing
Shelves: 2020
Remarkable!

I don't regard this one as an introductory book, but rather a "refresher" on the base of mathematics required for machine learning. Most of the book was a delight to read, I liked the slow building up of ideas in first chapters such as vector spaces, linear independence, basis, rank, linear mapping, inner products, orthogonality, .., etc. to the point that you become comfortable with them when used in more complex chapters, even if you had little or no background on them.

There were so
...more
Solomon Xie
Oct 12, 2018 rated it it was ok
Shelves: math
I'm so intimidated by the enormous amount of terms and symbols, and couldn't say it's a beginner friendly book. So i'm gonna let go of this and try other books ...more
Estefano Palacios
Brilliant and Precise

The book is the missing piece between books like Artificial Intelligence: A Modern Approach and mathematics. It is recommended that you've had exposure to the mathematical topics prior to reading the book, but let that stop you if you're a beginner. Having said that, a course on single variable calculus ought to be under your belt.
...more
Filip Karlo Došilović
Dec 02, 2020 rated it it was amazing
If there is a book to start your machine learning journey the right way, whether you are a mathematics or computer science student, this would be it.

A piece of advice: I would not recommend reading this book if you did not have exposure to calculus, introductory linear algebra and probability theory. You should view the first part of the book as a quick refresher, and not as an introduction to these subjects.
Nick Greenquist
Jan 24, 2019 rated it it was amazing
Very difficult mathematics book. However, I feel like I came out the other side with some new mathemetical skills in my toolbox and a better understanding of the theory behind many machine learning algrorithms. However, this book is definitely a very tough read.
Nadiantara Wayan
Jan 04, 2021 rated it it was amazing
Perfect and free, what else do you want?
SalKhan
Dec 28, 2020 rated it it was amazing
A must read for beginners in machine learning!
Tianyao Chen
May 01, 2020 rated it it was amazing  ·  review of another edition
Shelves: math
A great crash course to brush up the math required for ML!
Robin Dong
Mar 12, 2020 rated it really liked it
Just give this book a preliminary view.
It's easy to understand for most parts hence good for software developers (such as me)
...more
Ariyasacca
Jan 05, 2019 rated it it was amazing
awesome
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Using too much symbolic language? 2 13 Jan 20, 2019 09:31AM  

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