These 15 best books to learn artificial intelligence will help you understand the basics of AI and how it works in real life.
Now let’s discuss these 15 best books to learn Artificial Intelligence:
- Machine Learning: The New AI by Ethem Alpaydin
- Artificial Intelligence: The Basics by Kevin Warwick
- Artificial Intelligence – A Modern Approach by Stuart Russell & Peter Norvig
- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy
- Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald
- Machine Learning for Beginners by Chris Sebastian
- Machine Learning for Dummies by John Paul Mueller and Luca Massaron
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Make Your Own Neural Network by Tariq Rashid
- Artificial Intelligence for Humans By Jeff Heaton
- Introduction to Artificial Intelligence by Philip C Jackson
- How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
- Life 3.0 by Max Tegmark
- Deep learning in production by Sergios Karagianakos
- Machine learning engineering by Andriy Burkov
Machine Learning: The New AI by Ethem Alpaydin
Machine Learning: The New AI by Ethem Alpaydin gives an overview of machine learning. The book covers everything that a beginner should know about machine learning. The book starts with the evolution of machine learning and then moves toward the important algorithms backed up by examples. The book explains how digital technology has shifted from number-crunching devices to mobile phones. The book also details examples of the usage of AI in our daily life. The book ends with insights into the future trends in machine learning along with legal implications for security and privacy. Any person who has no idea about computer science or AI can get all the ideas from this book.
Artificial Intelligence: The Basics by Kevin Warwick
This book talks about different AI aspects and different methods of implementing it. The book explores the history of AI, its shift from old to new times, and its future. The book covers the functioning of modern AI technology and robotics. The book also provides recommendations for other books to strengthen your knowledge and understanding of AI. This book is perfect for beginners as it is quick and explores all issues in depth using simple language and stepwise procedures.
Artificial Intelligence – A Modern Approach by Stuart Russell & Peter Norvig
This book is considered the best book in the area of Artificial Intelligence. This book is specially designed for beginners in the field. The book is written in easy and comprehensible language and is less technical in nature. It covers almost all the basics of AI. The book covers algorithms, multi-agent systems, game theory, Natural Language Processing, and local search planning methods. The book touches upon all the basic topics without getting into complexities. A person new to this field must read this book to create a firm base for learning AI.
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy
Different data scientists recommend this book because it backs up the theory with practical examples. The book gives detailed explanations of machine learning approaches basically used in predictive analysis. This book’s four major approaches are detailed using simple language and without specific jargon. Each approach is explained using algorithms and technical models along with detailed samples. The book is best for those who have little knowledge of computer science, engineering, statistics, or any programming language.
Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald
This book explores different theoretical and practical aspects of machine learning using a simple and easy language. The writer didn’t use technical jargon to complicate things for beginners in the field of AI. The book provides clear and real-life examples with detailed explanations of various algorithms. The writer covered different AI aspects like sociological, ethical, humanitarian, and philosophical concepts. The book allows the readers to delve deep into the world of AI and know everything from scratch.
Machine Learning for Beginners by Chris Sebastian
As the title suggests, this book is written for beginners in the field of AI. The book traces the history of machine learning and explains its shift from ancient methods to today’s technological world. The book explains data and its usage by programmers to develop learning algorithms. All the basic concepts of AI, like neural networking and swarm intelligence, are explained in depth to create a firm base for beginners. The book uses simple examples to explain complex terms and mathematical calculations. The book also covers real-life situations that are making human lives easier and simpler.
Machine Learning for Dummies by John Paul Mueller and Luca Massaron
As the title suggests, this book is for beginners in machine learning. The book was written by two data science experts who made it easy for anyone to learn machine learning and implement it seamlessly. Machine learning is a really complicated field, but it requires a firm base to get hold of all complex ideas. This book covers all the basics of machine learning and its application to the real world. It also sheds light on coding in Python and R for the tech machines performing pattern-oriented tasks. The reader can understand the importance of machine learning through web searches, internet ads, fraud detection, etc.
The Hundred-Page Machine Learning Book by Andriy Burkov
Andriy Burkov’s “The Hundred-Page Machine Learning Book” is one of the best books in the field of artificial intelligence. It gives detailed explanations of all basics of machine learning. For advanced learners, it provides practical recommendations. The author shares his own personal and vast experience with the readers. The book covers all major topics concerning machine learning. The topics range from classical methods to modern methods in machine learning. If you want to understand the mathematical complexities behind machine learning, this book will help you a lot on this journey.
Make Your Own Neural Network by Tariq Rashid
Make Your Own Neural Network by Tariq Rashid is one book with a step-by-step guide for readers. This book explains the complexities of neural networking in an easy step-wise process. The book opens up with simple ideas and gradually moves towards the complex ideas of neural networks. The writer encourages readers to build their own neural networks using Python Language. The book comprises three parts. The first part details the mathematical ideas based on neural networks. The second part is practical in nature and concerns the use of the Python language. The final and last part gives a chance to the reader to delve into the mysterious link of the neural network.
Artificial Intelligence for Humans By Jeff Heaton
If you want to get an idea of artificial algorithms, this book will help you a lot. The main purpose of this book is to teach AI to those with little knowledge of mathematics. This book will make you an AI expert if you have a basic knowledge of algebra and computer programming. The book details basic algorithms like clustering, regression, distance metrics, and dimensionality. All the algorithms explained in this book use numeric calculations. The readers can easily practice all these algorithms through the examples provided in the book by Jeff Heaton.
Introduction to Artificial Intelligence by Philip C Jackson
Introduction to Artificial Intelligence talks about computer reasoning processes, research of the last two decades, and their results. This book provides detailed yet easy-to-follow problem-solving techniques and representation models. The book by Philip Jackson holds great importance in the field of AI due to the coverage of all basics like game playing, different models of AI, automated procedures of understanding Natural Language Heuristic search theory, heuristic sense analysis, and robot systems. The book provides a comprehensive overview of all major aspects concerning AI.
How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
In How to Create a Mind, Kurzweil interlinks the human mind with technological advancement. He explains the working of the human brain in detail and then usage of that brain to create more powerful and intelligent machines. The writer discusses the functioning of the brain and how the mind emerges from the brain. The writer also highlights the power of human intelligence, specifically emotional and moral intelligence, and links it with the intelligent machines that humans are creating.
Life 3.0 by Max Tegmark
Max Tegmark’s ‘Life 3.0’ helps readers dive into the world of AI. The book covers the broader aspects of AI concerning superintelligence, the physical limits of AI, and machine consciousness. The book also talks about the societal issues that are emerging with the use of AI. The writer says that AI has the power to transform our future. The writer asks different questions concerning the vast use of AI in today’s world and tries to link human life with the deep interference of AI.
Deep learning in production by Sergios Karagianakos
Deep learning in production by Sergios takes a hands-on approach to MLOps. The book starts off with a vanille deep learning model and then moves towards building a web application. This book is an excellent hands-on practice for ml researchers having very little software knowledge. The book is divided into several chapters, and each chapter talks about a different machine-learning phase. The reader will learn to write deep learning code such as unit testing and debugging. The book also teaches ways to build data pipelines and deployment techniques by focusing on tools like uWSGI, Nginx, Docker, and Flask. The book ends with an exploration of MLOPs.
Machine learning engineering by Andriy Burkov
This book by Burkov is a great book for learning the machine learning lifecycle. The writer helps the readers to build machine learning applications. The book comprises different chapters, and each chapter discusses a separate machine-learning phase. The book starts off with the ‘Design Phase’, which discusses the priorities and challenges of any machine learning project. The book then discusses common mistakes made in ML and their solutions. The next phase is ‘Training and Evaluation’, divided into three chapters. Here, Burkov explains how to improve the model accuracy using regularization, hyperparameter tuning, and other techniques.
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