Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python

by Jason Brownlee

Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.

Genres: Mathematics, Artificial Intelligence, Computer Science, Programming

211 pages, ebook

Published January 1, 2018

About the author

Jason Brownlee, Ph.D. trained and worked as a research scientist and software engineer for many years (e.g. enterprise, R&D, and scientific computing), and is known online for his work on Computational Intelligence (e.g. Clever Algorithms), Machine Learning and Deep Learning (e.g. Machine Learning Mastery, sold in 2021) and Python Concurrency (e.g. Super Fast Python).

Jason writes fiction under the pseudonym J.D. Brownlee: https://www.goodreads.com/jdbrownlee

Reviews

[Aniket Patil · Rating 3 out of 5]

This book is a very good introductory book on this topic. It is divided into several topics, Introduction, Foundation, Numpy,Matrices,Factorization and statistics. Book gives fairly good information on these topics and subtopics under it.

Author collected information from various machine learning, deep learning, mathematics and statistics books. He quoted some useful info from other books (which saves our valuable time).

Initially Introductory concepts are explained in a very good manner. Later on Numpy codes are given per example. Why matrices are used, what are vectors? what are tensors? types of matrices ? such questions are answered in simple language.

Author has mentioned that, one can read book cover to cover. I did same. Its less boring compared to other topic specific books. While coming to end, I realized drop in my energy, probably due to too much concepts, but as book mentions its basics and I was in search of basics. So, it scores well on this.

I would have given 4 stars , had author given more information about various techniques comparisons. Which to use in which cases etc. (at some point such information is there, but is not sufficient). But overall a good book to start with or if one knows about the topics, its good revision book as well.

[Arun · Rating 4 out of 5]

Hands-on in terms of programming the math concepts using python with high level explanation of math theory behind them but more importantly prepare you to be productive in using ML concepts sooner than later. This is the top down approach to learning math pre-requisite for machine learning. This is in contrast to the bottom up approach of learning the math (Linear algebra, Calculus, etc) independent of how it is applied in ML practice. The final chapter brings all together with the explanation of linear regression and how we can use different matrix decomposition methods called QR decomposition or SVD decomposition to arrive at the best estimate of coefficient vectors (via the minimization of error in estimation a.k.a least squares) of a function with one independent variable and one dependent variable, basically y = f(x).

[Max Domingo · Rating 4 out of 5]

Good recap of linear algebra. I would have loved that it had more indepth explanations for the later units and went through the basics a bit faster.

[Durgesh · Rating 4 out of 5]

Jason Brownlee is amazing writer. He explained all concepts very well.
Highly recommended !!!

[Masha]

just using this for the page count, I actually did the imperial college london MOOC on coursera

[Vladimir · Rating 5 out of 5]

Jason Brownlee has a great talent for explaining complex topic in a way that's easy to understand. the book makes it possible for developers to get into the amazing world of machine learning with very little friction. Highly recommended