Overview
“Math Handbook for Data Science, Machine Learning & AI” is a complete re-write of the popular “Math Handbook for ML”. This book took 2 years to write and edit, including 3 new chapters! This is an excellent reference with common math rules, e.g., algebraic rules for exponents, derivations for linear algebra and probability theory. Great pains have been taken to compile a large body of mathematics literature into one concise and consistent notation.
A free book chapter is available here.
Reviews
“I intend on highly recommending your book to our analysts, engineers, and the local work libraries.” - Dr. Annette Fisher
“This book succinctly explains the derivations of many things often skipped in other texts. I wish I had this as a reference many years ago.” - Dr. Luke Diaz
Notes for the New Edition
This new edition was completely rewritten to make the text clear, more engaging and to improve technical accuracy.
Each chapter concludes with a section on common math errors, e.g., “Common Algebraic Errors.” This is very useful as a reminder of mistakes people often make.
The original manuscript was unfriendly to colorblind readers; in particular red/green contrasts are troublesome. The new color palette uses orange/blue and red/blue contrast.
We are also working to make this book the first of its kind available in Braille. A significant level of effort was invested to create a new math library in Braille that augments or replaces Neimeth Code. This will help a significantly under-served community of blind researchers, engineers, and scientists.
To minimize waste and environmental impact in the printing process, this book is printed in the 8.5” x 5.5” format.
To make this book extra special, it is printed with a premium full-color gloss hardcover, and premium 93 opacity 60lb pages with full-color figures.
Book Details
Publisher: Fibonacci Press
Language: English
Hardback: 159 pages
ISBN: 979-8-3178-2607-9
Dimensions: 5.5 x 8.5 x 1.2 inches
Publish date: 12/2023, revised 03/2025
Overview
“Math Handbook for Data Science, Machine Learning & AI” is a complete re-write of the popular “Math Handbook for ML”. This book took 2 years to write and edit, including 3 new chapters! This is an excellent reference with common math rules, e.g., algebraic rules for exponents, derivations for linear algebra and probability theory. Great pains have been taken to compile a large body of mathematics literature into one concise and consistent notation.
A free book chapter is available here.
Reviews
“I intend on highly recommending your book to our analysts, engineers, and the local work libraries.” - Dr. Annette Fisher
“This book succinctly explains the derivations of many things often skipped in other texts. I wish I had this as a reference many years ago.” - Dr. Luke Diaz
Notes for the New Edition
This new edition was completely rewritten to make the text clear, more engaging and to improve technical accuracy.
Each chapter concludes with a section on common math errors, e.g., “Common Algebraic Errors.” This is very useful as a reminder of mistakes people often make.
The original manuscript was unfriendly to colorblind readers; in particular red/green contrasts are troublesome. The new color palette uses orange/blue and red/blue contrast.
We are also working to make this book the first of its kind available in Braille. A significant level of effort was invested to create a new math library in Braille that augments or replaces Neimeth Code. This will help a significantly under-served community of blind researchers, engineers, and scientists.
To minimize waste and environmental impact in the printing process, this book is printed in the 8.5” x 5.5” format.
To make this book extra special, it is printed with a premium full-color gloss hardcover, and premium 93 opacity 60lb pages with full-color figures.
Book Details
Publisher: Fibonacci Press
Language: English
Hardback: 159 pages
ISBN: 979-8-3178-2607-9
Dimensions: 5.5 x 8.5 x 1.2 inches
Publish date: 12/2023, revised 03/2025
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