This page contains a list of books I personally found helpful to understand AI in a better way. Hope you like them too!
Hands-on Artificial Intelligence for Cybersecurity
This book uses a particular kind of example and shows its execution using different ML models. It also benchmarks the results thereby helping the reader understand the significance of each model.
Artificial Intelligence and Machine Learning Fundamentals
If you’re looking for a book that gives a holistic picture of AI and ML models with decision making algorithms and concepts sufficed with activities and exercises, this book is the one. Bet you will reap the most out of it as a beginner or may be even as a refresher handbook.
Machine Learning Mastery series by Jason Brownlee
A series of books brought out by the pioneer, Jason Brownlee aims at improving developing experience in machine learning. His books on implementing machine learning algorithms and learning basics of linear algebra can help enthusiastic and curious developers to understand the algorithms conceptually and development of advanced algorithms.
Machine Learning for High-Risk Applications
This is a very fresh book out of the oven and I had just started reading it. So far I have found this book insightful and it gives the real picture of contemporary AI projects w.r.t., responsible AI. It has two parts in the book. While the first part of this book discusses the best practices and pending regulations towards risk management of AI projects, the second part of the book suffices part one with code examples regarding bias testing, remediation, attacks, and countermeasures. A must read for secure AI enthusiasts and practitioners.