Free AI Books on Machine Learning, AI and Data Science

 A subfield of Computer Sciences, Artificial Intelligence (AI) is becoming a vital part of the world around us. Consequently, anyone who wants to advance in the field should expand their knowledge of AI by reading some of the seminal books in the area. Below are some free online books on Machine Learning, AI and Data Science that should be on the must-read list for any serious ML/AI enthusiast.


In this book, you learn the fundamental concepts of machine learning in a clear and intuitive way. Each chapter includes examples and exercises, making this book ideal for students and beginners who want to quickly understand the theory behind each concept. This book also helps readers improve their skills in coding with the use of Python, one of the most popular languages for implementing machine learning algorithms.


This book provides a comprehensive introduction to reinforcement learning. The authors have developed a straightforward interpretation of the key ideas and algorithms in this rapidly evolving field, while explaining how reinforcement learning is related to psychology and neuroscience. The book also covers important topics such as model evaluation, distribution shift and a/b testing. In addition, it explains how to train and evaluate deep neural networks with Sklearn, a powerful open-source framework that is often used in industry.


Machine learning is the investigation of computer frameworks that gain from information and experience. It is connected in an unbelievable assortment of utilization zones, from medication to publicizing, from the military to the person on foot. This book introduces the fundamental theories and methodologies of this field, from its earliest philosophical and mathematical roots to tantalizing glimpses of 21st-century machines with prodigious intelligence and speed.


Written by industry leaders Ian Goodfellow, Yoshua Bengio and Aaron Courville, this is a popular textbook that introduces the reader to the field of machine learning in general and deep learning in particular. The latest version of this book includes many new examples and exercises, and updates the theory and code in the chapters on linear algebra, probability theory, different neural network architectures, and deep learning research.


The book starts with the basics, including the fundamentals of linear algebra, probability theory and different neural network architectures. It then moves on to the practical side of machine learning, showing how to implement various algorithms and models in the programming language Python. The book is accompanied by extensive code samples, including a full-blown example of an object detection algorithm.


The author takes you on a journey to explore the fascinating and exciting applications of artificial intelligence in business, engineering, social sciences, humanities and natural sciences. He focuses on the impact that AI is having on business and society and how to leverage it to your advantage. He shows you how to build, test and deploy intelligent agents, as well as the key issues in deploying AI. Finally, he provides strategies for how to design AI with ethical considerations in mind and discusses the current state of the art in this area.Free AI books


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