Be inspired. Live your potential!
Learn and understand the fundamentals in AI, machine learning, maths, and engineering with several books and resources that are easy to read and understand at all experience levels.
Books
Each of these books include examples, worked problems, and code written in both Matlab and Python.
A Comprehensive Approach to Data Science, Machine Learning & AI Available in March 2025! This forthcoming title is the result of 5 years of writing and editing to create the most comprehensive text available for novice and experts alike. 58 AI and ML algorithms are discussed in detail with 264 from-scratch tutorials in Matlab, Python, and R, revealing the fundamentals usually hidden in libraries, and math derivations and proofs not found in other texts on AI/ML. You will not want to miss this book!
Special Edition! Math Refresher for Data Science, Machine Learning & AI is a complete re-write of the popular “Math Refresher for ML”. This book took over 4 years to write and edit, including 6 new chapters! It is expanded for a wider audience and is a great review prior to the first year of graduate school, or as a refresher for the working professional looking to quickly refresh their skills prior to more difficult engineering and scientific research.
Special Edition! 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.
How to do Research, Remotely! summarizes decades of research and hundreds of publications on human psychology, research habits, effective learning & study skills, effective presentations, and effective writing skills. Dr. Roysdon also added his personal experience as a researcher, author and leader.
Optimal Nonlinear Bayesian Estimation & Sensor Fusion introduces derives optimal algorithms for nonlinear Bayesian estimation and outlier detection in multi-sensor real-time autonomous or robotic systems.
The Absolute Beginners Guide to Neural Networks is a simple tutorial of a basic Artificial Neural Network, with worked examples in Python, so that anyone can implement a Neural Network from scratch!
Math Refresher for Machine Learning is sold out and replaced with the greatly expanded text Math Refresher for AI & Machine Learning.
Math Handbook for Machine Learning is sold out and replaced with the greatly expanded text Math Handbook for AI & Machine Learning.
Extra Resources
Please visit the GitHub resources page for free chapter examples, code examples, technical notes, cheat sheets, and more.