Mathematics for Machine Learning Paperback – 23 April 2020

You can train all the models that you want but if you don't understand its building blocks, that is, mathematics, you will be lost. This book is a great resource which helps you ease into the world of mathematics behind machine learning in a very simple way.

Wide ranging with basic principles of foundation in maths and machine learning

I have just received this book hours ago. It is paperback. I breezed through book with skimreading at first. Simple illustrations with few colours — VERY HELPFUL. The page layout is perfect, very easy on my eyes! I could read quickly, as the text is not too cluttered. I have learned maths fast. Thank you, authors.Moreover, I absolutely love the 4cm margins at the outer edges of pages, as I like pencilling my notes in blank spaces or place sticky notes there. Helpful footnotes in the margins.

I have a PhD in ML and a CS background. My stats knowledge is lacking so I was hoping this book could help me get a better understanding of the core foundational concepts in ML.In the first few chapters (Part 1 of the book) there is a lot of skimming over the math which makes it difficult for me to learn. I have to spend more time looking at other sources to fill in the blanks.Part 2 is a lot easier to read. I enjoyed these chapters a lot more.

It's a very good (and maybe only) resource for someone who's starting on the field of machine learning and is trying to understand the underlying mathematics.

Good for ml enthusiast's. All the important maths subjects are covered

While the content of the book is very clear and concise (something many maths books tend to struggle with), this text unfortunately falls into the trap of presenting many new ideas with too few exercises to reinforce them (at least in the maths section). This would be fine if the authors' aim was to produce a reference for these topics, but they themselves acknowledge the intended target audience to be "undergraduate students, evening learners and learners participating in online machine learning courses" i.e. those who are being introduced to the subject matter for the first time and who will quickly forget these ideas after having struggled through the terse, potentially unfamiliar, language.

It’s a bit terse to read at times but worth persevering through. The book contains good detail of key mathematical concepts and their uses in machine learning