Discover the 35+ Best FREE AI Books catering to all levels. Unravel the wonders of AI, primers to cutting-edge research, and explore its transformative potential.
Beginner Level
The Hundred-Page Machine Learning Book - Andriy Burkov
Description: Are 100 pages enough to conquer machine learning? This free eBook is one of the best book for artificial intelligence for beginners and guarantees to provide you with almost all the essential information for beginners. The author promises the book will teach you how machine learning works so you can build complex AI systems and pass an interview.
Interpretable Machine Learning. A Guide for Making Black Box Models Explainable - Christoph Molnar
Description: If you want to learn how to make machine learning decisions interpretable, this is the eBook for you! It details how to select and apply the best interpretation methods for any machine learning project, making it a valuable source of knowledge for data scientists, statisticians, machine learning engineers, and anyone interested in machine learning.
Python Data Science Handbook - Jake VanderPlas
Description: Python is a first-class tool for any data scientist. This book teaches you how to use its essential tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and many others. It's a perfect resource for tackling day-to-day issues such as cleaning, manipulating, and transforming data or building machine learning models.
Machine Learning Yearning - Andrew Ng
Description: This free eBook, created by one of the most popular personalities in the AI industry, is focused on structuring machine learning projects. It explains how to make machine learning algorithms work, identify and prioritise the most promising aspects of your AI projects, diagnose errors in your ML systems, and perform several other vital tasks.
How to implement AI in your company - Przemysław Majewski, Katarzyna Rojewska, Emilia Brzozowska, Bertie Conibear
Description: This free guide is dedicated to all businesses considering implementing artificial intelligence. It explains all key AI concepts, the necessary steps, insights, and data from the latest industry reports, along with helpful advice and suggestions.
Artificial Intelligence through Prolog - Neil C. Rowe
Description: Artificial intelligence is a hard subject to learn, but the author has written this book to make it easier. He explains complex concepts in a simple, concrete way, and it is intended for all first courses in artificial intelligence.
A Brief Introduction to Neural Networks - David Kriesel
Description: This book gives a profound insight into the paradigm of neural networks, including LATEX. The content is continuously extended, meaning the information will stay up-to-date.
Ethical Artificial Intelligence - Bill Hibbard
Description: While improving your AI skills is always good, reading about the technology’s ethical challenges is also valuable. The author presents the technical challenges of designing ethical AI and then makes a case for various strategies for solving these issues. The book is easy to understand, with mathematical explanations available for those who want that level of detail.
Computers and Thought: A Practical Introduction to Artificial Intelligence - Mike Sharples et al.
Description: This text aims to help readers with little or no background in computing to cognitive science and artificial intelligence (AI). It focuses on AI's psychological, philosophical, and social effects.
Intermediate Level 1
Artificial Intelligence: Foundations of Computational Agents - David Poole, Alan Mackworth
Description: This book introduces AI as the study of designing intelligent computational agents. It serves a wide variety of readers, including professionals and researchers.
Mathematics for Machine Learning - Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Description: Published by Cambridge University Press, this book motivates people to learn mathematical concepts and provides the necessary skills to read other books on advanced machine learning techniques. It is split into two parts: mathematical foundations and example machine learning algorithms that use the mathematical foundations.
Artificial Intelligence – Agents and Environments - William John Teahan
Description: This book is the first in a series on Artificial Intelligence. It introduces the topic, emphasising the use of agent-oriented design. Topics include agents, environments, agent movement, and agent embodiment.
Artificial Intelligence – Agent Behaviour - William John Teahan
Description: This book adopts a behaviour-based approach to the design of agent-oriented systems. The topics from a behaviour-based perspective include agent communication, searching, knowledge and reasoning, and intelligence.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie, Robert Tibshirani, Jerome Friedman
Description: The science of learning plays a crucial role in statistics, data mining, AI, and other disciplines. This book emphasises concepts over pure mathematics and focuses on supervised learning (prediction) and unsupervised learning, covering topics like neural networks, classification trees, support vector machines, and boosting.
Autonomous Agents - Vedran Kordic
Description: The field of multi-agent systems investigates the process underlying distributed problem-solving and designs some protocols and mechanisms involved in this process. This book presents an overview of the research issues in multi-agents.
Computational Intelligence and Modern Heuristics - Al-Dahoud Ali
Description: This book takes readers on a stunning voyage of computational intelligence heuristics research and applications. It covers various computational intelligence techniques, including neural networks, fuzzy logic, genetic algorithms, etc.
Artificial Intelligence and Molecular Biology - Lawrence Hunter
Description: This book offers a current sampling of AI approaches to problems of biological significance. It covers genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and the simulation of biological systems.
Brief Introduction to Educational Implications of Artificial Intelligence - David Moursund
Description: This book is designed to help teachers learn about the educational implications of current uses of Artificial Intelligence to solve problems and accomplish tasks. It is intended for self-study or use in workshops.
Intermediate Level 2
Neural Networks and Deep Learning - Michael Nielsen
Description: Highly recommended if you want to learn about deep learning and neural networks. This book will help you better understand the two topics and how to build a deep neural network from scratch. It’s the perfect choice for beginners who want a robust grounding in the core principles of these complex subjects.
Reinforcement Learning - Richard S. Sutton, Andrew G. Barto
Description: This book delivers a straightforward interpretation of the field’s key ideas and algorithms. It covers reinforcement learning without going beyond the tabular case for which exact solutions can be found. Also, it discusses how reinforcement learning relates to psychology and neuroscience, along with future societal impacts.
The Quest for Artificial Intelligence - Nils J. Nilsson
Description: Artificial intelligence is a field within computer science attempting to build enhanced intelligence into computer systems. This book traces the subject's history, from the eighteenth-century pioneers to the work of today's AI engineers.
Ambient Intelligence - Felix Jesus Villanueva Molina
Description: This book analyses open problems key to making ambient intelligence a reality. It gives the reader a good idea about the current research lines in ambient intelligence.
The Boundaries of Humanity: Humans, Animals, Machines - J. Sheehan, M. Sosna
Description: The relatively new fields of sociobiology and artificial intelligence bring new insights to the age-old debate over what it means to be human. The book explores what these two fields have in common and how they have affected how we define humanity.
Dive into Deep Learning - Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola
Description: The first eBook on our must-read list is deep diving into deep learning. The authors are Amazon employees who use Amazon’s MXNet library to teach Deep Learning. They update their work regularly and have recently added new implementations to the book in two popular DL libraries: Pytorch and Tensorflow/Keras.
Advanced Level 1
Virtual Reality: Human-Computer Interaction - Xin-Xing Tang (ed.)
Description: Virtual reality significantly affects information organisation and management and even changes the design principle of information systems. The book aims to provide a broader perspective on virtual reality.
Foundations of Constraint Satisfaction - Edward Tsang
Description: This book was the first attempt to define the field of constraint satisfaction. It covers the subject's theoretical and implementation aspects, providing a framework for studying this field and relating it to different research.
Swarm Intelligence: Focus on Ant and Particle Swarm Optimization - Felix T.S. Chan, Manoj K. Tiwari
Description: Motivated by the capability of biologically inspired algorithms, the book aims to present recent developments in optimization with swarm intelligence techniques. It also offers some selected representative case studies.
Encyclopedia of Computational Intelligence - Eugene M. Izhikevich, et al.
Description: This book covers various topics related to computational intelligence, such as neural networks, evolutionary computation, robotics, machine learning and pattern recognition, graphs and complexity, artificial intelligence, information theory, fuzzy systems, signal analysis, etc.
Artificial Intelligence and Responsive Optimization - M. Khoshnevisan, S. Bhattacharya, F. Smarandache
Description: This book aims to apply Artificial Intelligence and control systems to different accurate models. It is intended for graduate students and researchers who are active in the applications of AI and Control Systems in modelling.
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations - Yoav Shoham, Kevin Leyton-Brown
Description: Multiagent systems consist of multiple autonomous entities having different information and diverging interests. This comprehensive introduction to the field offers a computer science perspective and draws on game theory ideas.
An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Description: This eBook, recommended by the 2014 Eric Ziegel Award winner from Technometrics, includes an introduction to statistical learning methods and several R labs. It offers elaborate explanations on implementing particular methods in real-life settings and is an excellent resource for students and practising data scientists looking to improve their skills.
Advanced Level 2
Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
Description: Let’s stick with the subject of Deep Learning. The authors created this resource to help beginners enter the field of machine learning, with a focus on deep understanding. Interestingly, one of the authors – Yoshua Bengio, won the 2018 Turing Award (the Nobel Prize for computing) for his work in deep learning.
Deep Learning with PyTorch - Eli Stevens, Luca Antiga, Thomas Viehmann
Description: If you plan to build neural networks with PyTorch, you’ll want to begin your journey with this popular, open-source machine-learning framework. The eBook provides a great introduction to the subject, sharing practical knowledge related to pre-trained networks, how to use a neural network and convolutions, deploy a model to production, and much more.
Affective Computing - Jimmy Or
Description: An overview of state-of-the-art research in Affective Computing. It presents new ideas, original results, and practical experiences in this increasingly important research field, consisting of 23 chapters categorised into four sections.
10 Machine Learning Frameworks to Try - DLabs.AI Team
Description: This eBook provides deeper insight into ML frameworks that can increase efficiency and decrease work time. It recommends ten ML frameworks to check and valuable tips on why to use a particular framework or when to avoid one.
How to build AI-driven object detection software - Maciej Karpicz, Marek Orliński, Mariusz Rzepka, Michał Wojczulis
Description: This eBook stretches to 75 pages and is a treasure trove of knowledge from big data and machine learning experts. It includes step-by-step instructions on how to build object detection software using deep learning and synthetic data.
Expert Level
Fault Detection - Wei Zhang
Description: Fault diagnosis technology is a synthetic technology related to several subjects: modern control theory, reliability theory, mathematical statistics, fuzzy set theory, information handling, pattern recognition, and artificial intelligence.
Deep Learning for Coders with FastAI and PyTorch: AI Applications Without a PhD - Jeremy Howard, Sylvain Gugger
Description: This hands-on guide demonstrates that programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code.
If you're looking for more books we have other lists for you to try from - 22 best machine learning books to read for 2023
