20 Best Artificial Intelligence Books For Beginners in 2025

Summary: This curated list of 20 Artificial Intelligence books for beginners highlights foundational concepts, coding practices, and ethical insights. Perfect for newcomers, these books provide practical examples, step-by-step guidance, and real-world applications to build a strong understanding of AI and its transformative potential across industries.

Introduction

Artificial Intelligence (AI) continues to shape the future, with its market size skyrocketing from $515.31 billion in 2023 to a projected $2,740.46 billion by 2032, growing at an impressive CAGR of 20.4%. As AI becomes integral to industries worldwide, foundational knowledge is more critical than ever. 

For beginners, understanding the basics is the first step toward leveraging this transformative technology. This blog highlights the 20 best Artificial Intelligence books tailored for newcomers, offering practical insights, ethical considerations, and real-world applications. We aim to guide readers in choosing the best resources to kickstart their AI learning journey effectively.

Key Takeaways

• These books cover foundational concepts, coding, and ethics for beginners.
• From neural networks to real-world AI applications, explore a range of subjects.
• Many books offer hands-on exercises and coding examples for effective learning.
• Simplified language ensures accessibility, even for non-technical readers.
• Gain knowledge to leverage AI’s potential and navigate its ethical challenges.

Selection Criteria

We carefully selected books that cater to beginners by prioritising clear and straightforward language, ensuring concepts are easy to understand. We focused on titles with practical examples and hands-on exercises, allowing readers to apply their learning effectively. 

Each book was chosen for its relevance in 2025, reflecting the latest AI trends, technologies, and real-world applications. We also emphasised diversity in topics, covering foundational concepts, coding practices, and ethical considerations. 

By highlighting approachable content and actionable insights, we aimed to ensure these books provide a comprehensive starting point for anyone stepping into the world of Artificial Intelligence.

List of 20 Best Artificial Intelligence Books

Artificial Intelligence (AI) has become integral to modern technology, transforming industries and shaping the future. For beginners stepping into this fascinating field, a well-rounded understanding starts with the right resources. 

This curated list of AI books spans foundational concepts, practical applications, and thought-provoking discussions to equip readers with the knowledge and skills to embark on their AI journey.

Artificial Intelligence – A Modern Approach 

By Stuart Russell & Peter Norvig

This book is widely considered the gold standard for AI enthusiasts. With clear and engaging writing, it covers a range of topics, from basic AI principles to advanced concepts. Readers will gain a solid foundation in search algorithms, game theory, multi-agent systems, and more.

Key Features:

• Comprehensive coverage of AI fundamentals and advanced topics.
• Explains search algorithms and game theory.
• Includes statistical natural language processing techniques.
• Easy-to-understand examples and explanations.
• Suitable for both beginners and advanced learners.

Machine Learning for Dummies

By John Paul Mueller and Luca Massaron

This book introduces the basics of Machine Learning with practical examples. Using simple language, it explains how to perform data analysis and pattern recognition with Python and R. It’s perfect for those with minimal coding experience.

Key Features:

• Easy-to-follow introduction to Machine Learning.
• Practical examples using Python and R.
• Explains real-world applications like fraud detection.
• Covers basic Machine Learning concepts.
• Authored by experienced data scientists.

Artificial Intelligence and Machine Learning

By Vinod Chandra S. S.

Targeted at undergraduate students, this book bridges the gap between AI and Machine Learning. It explains reinforcement, supervised, and unsupervised learning with case studies and examples.

Key Features:

• Covers AI and Machine Learning basics.
• Detailed algorithms and pseudo-codes.
• Includes case studies for practical understanding.
• Explains reinforcement learning techniques.
• Tailored for students and beginners.

Make Your Own Neural Network

By Tariq Rashid

This book offers a step-by-step guide to understanding neural networks, from basic concepts to building your own using Python. It’s divided into foundational mathematics, practical implementation, and exploring neural networks’ inner workings. An excellent resource for beginners and hobbyists.

Key Features:

• A comprehensive introduction to neural networks.
• Includes Python-based coding exercises.
• Explains underlying mathematical concepts.
• Encourages hands-on learning.
• Covers applications using Raspberry Pi.

Machine Learning: The New AI

By Ethem Alpaydin

This concise guide explores the evolution of Machine Learning, key algorithms, and real-world applications. It contextualises the rise of AI and Machine Learning in our digital world while addressing its future impact and ethical considerations, making it accessible to readers without a technical background.

Key Features:

• Overview of Machine Learning Algorithms.
• Discusses AI’s impact on daily life.
• Ethical implications of AI highlighted.
• Ideal for readers without a technical background.
• Explores future applications of Machine Learning.

Fundamentals of Machine Learning for Predictive Data Analytics

By John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

This book breaks down predictive data analytics using four major Machine Learning approaches. Each concept is supported by algorithms, mathematical models, and case studies, making it ideal for readers with a basic understanding of mathematics or computer science.

Key Features:

• Covers four main Machine Learning approaches.
• Provides algorithms and mathematical models.
• Illustrated with detailed examples.
• Suitable for readers with basic technical knowledge.
• Practical insights into predictive analytics.

The Hundred-Page Machine Learning Book

By Andriy Burkov

This compact yet comprehensive guide introduces Machine Learning fundamentals for beginners while offering advanced insights for professionals. The book covers key approaches like deep learning, random forests, and logistic regression, seamlessly blending theoretical and practical knowledge.

Key Features:

• Concise yet comprehensive.
• Covers all primary Machine Learning techniques.
• Includes practical insights from the author’s experience.
• Accessible for beginners and professionals alike.
• Focuses on mathematical foundations of algorithms.

Artificial Intelligence for Humans

By Jeff Heaton

This book demystifies AI algorithms for readers with basic programming and algebra knowledge. It simplifies complex AI topics like clustering, dimensionality, and regression, providing practical examples and numeric calculations to enhance understanding.

Key Features:

• Explains AI algorithms like clustering and regression.
• Minimal technical jargon.
• Provides numeric calculations for hands-on learning.
• Suitable for those with basic programming knowledge.
• Offers real-world use cases.

Machine Learning for Beginners

By Chris Sebastian

Aimed at beginners, this book delves into the history and fundamentals of Machine Learning. It explains concepts like neural networks, big data, and swarm intelligence with real-world applications and simplified math examples, making complex topics accessible.

Key Features:

• Traces the evolution of Machine Learning.
• Explains big data’s role in AI.
• Includes real-world applications.
• Simplifies complex mathematical concepts.
• Perfect for absolute beginners.

Artificial Intelligence: The Basics

By Kevin Warwick

This book provides a foundational overview of AI, exploring its history, present applications, and future potential. It offers insights into robotics and modern AI technologies, making it a quick and engaging read for anyone interested in AI.

Key Features:

• Covers AI history and advancements.
• Explores robotics and modern AI technologies.
• Offers recommendations for further reading.
• Suitable for readers seeking a broad overview.
• Discusses philosophical aspects of AI.

Machine Learning for Absolute Beginners

By Oliver Theobald

This beginner-friendly book explains Machine Learning concepts using plain English and visual examples. It covers theoretical and practical aspects, providing accessible insights for readers interested in applying Machine Learning in business and other domains.

Key Features:

• Focuses on simplicity and clarity.
• Includes visual aids for understanding algorithms.
• Ideal for business applications of AI.
• Offers a gentle introduction to Machine Learning techniques.
• Tailored for non-technical audiences.

Applied Artificial Intelligence: A Handbook for Business Leaders

By Mariya Yao, Adelyn Zhou, and Marlene Jia

This book focuses on the strategic use of AI for business leaders. It explains how Machine Learning and AI can drive operational efficiency and societal improvements while offering practical tips for implementation.

Key Features:

• A practical guide for business professionals.
• Focuses on AI’s strategic implementation.
• Covers Machine Learning applications.
• Explores societal impacts of AI.
• Offers actionable insights for decision-makers.

Advances in Financial Machine Learning

By Marcos Lopez de Prado

Targeting finance professionals, this book delves into using AI for structuring big data, leveraging supercomputing, and minimising errors in financial research. It combines mathematical explanations with practical applications for real-world economic problems.

Key Features:

• Targets financial applications of AI.
• Includes mathematical explanations.
• Discusses structuring Big Data for AI.
• Offers real-world examples.
• Suitable for professionals in finance and data science.

Life 3.0: Being Human in the Age of Artificial Intelligence

By Max Tegmark

“Life 3.0” explores AI’s societal, ethical, and existential implications. It examines how AI could impact crime, jobs, and humanity’s future, offering thought-provoking insights from an MIT professor leading research on AI safety and benefits.

Key Features:

• Explores AI’s impact on humanity.
• Discusses ethical challenges of AI.
• Covers automation and job markets.
• Engaging writing style.
• Ideal for thought leaders and futurists.

Artificial Intelligence Basics: A Non-Technical Introduction

By Tom Taulli

This book offers a beginner-friendly overview of AI concepts like deep learning, NLP, and robotics. It includes case studies and explores societal impacts, making it ideal for readers looking to understand AI’s broader implications.

Key Features:

• Covers basic AI concepts.
• Non-technical explanations.
• Real-world case studies included.
• Explores AI’s societal implications.
• Suitable for readers from all backgrounds.

A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going

By Mike Wooldridge

This book provides a fascinating look at AI’s history, present state, and future trajectory. Written by an AI researcher, it examines groundbreaking advancements and the practical implications of technologies like driverless cars and automated tools.

Key Features:

• Covers AI history and future trends.
• Accessible and fun to read.
• Discusses modern AI applications.
• Provides insights into AI’s challenges.
• Ideal for a general audience.

Artificial Intelligence Engines

By James V. Stone

This book explains neural networks and deep learning in a richly illustrated, accessible manner. It includes hands-on tutorials and examples, making it ideal for learners interested in exploring the mathematical engines driving AI.

Key Features:

• Covers historical and modern neural networks.
• Includes detailed mathematical analyses.
• Practical programming exercises.
• Richly illustrated for better understanding.
• Suitable for advanced beginners.

On Intelligence

By Jeff Hawkins

Jeff Hawkins, known for creating the PalmPilot, presents a groundbreaking intelligence theory in this book. Unlike traditional views that compare the brain to a computer, Hawkins argues that the brain is a memory-prediction system. 

This system stores sequences of events and relationships, enabling predictions that form the basis of intelligence and perception. The book also explores the implications of these ideas for building intelligent machines that surpass human capabilities.

Key Features:

• A unique perspective on intelligence.
• Explains memory-prediction theory.
• Discusses building intelligent machines.
• Written in an engaging style.
• Suitable for readers interested in neuroscience and AI.

The Alignment Problem: Machine Learning and Human Values

By Brian Christian

Brian Christian delves into the ethical and technical challenges of modern AI systems. As Machine Learning systems make more decisions in critical areas like hiring, justice, and healthcare, they often reflect unintended biases. 

This book highlights the “alignment problem,” where AI’s objectives diverge from human intentions, and examines efforts to address this issue through ethics and engineering.

Key Features:

• Focuses on ethical AI development.
• Highlights real-world AI challenges.
• Includes historical and modern AI examples.
• Explores bias and societal impact.
• Engaging and interdisciplinary.

Artificial Intelligence By Example

By Denis Rothman

This hands-on guide is designed for developers and AI enthusiasts who want to build practical AI solutions from scratch. Through real-world examples like chatbots, neural networks, and blockchain applications, the book introduces AI concepts and their implementation. It also covers advanced topics like meta-learning models and quantum computing circuits.

Key Features:

• Covers a wide range of AI topics.
• Focuses on practical implementation.
• Includes real-world examples and case studies.
• Explains advanced neural network models.
• Suitable for developers and AI enthusiasts.

This comprehensive list allows beginners to explore AI from multiple perspectives, ensuring a well-rounded understanding of this transformative technology.

Wrapping Up

Artificial Intelligence books serve as gateways to understanding this transformative technology. The curated list of 20 AI books offers practical insights, ethical considerations, and hands-on learning opportunities for beginners. 

From foundational concepts to real-world applications, these resources cater to various learning preferences, ensuring a solid starting point for anyone entering AI. By diving into these expertly selected books, readers can develop essential AI skills and knowledge, empowering them to embrace AI’s potential in shaping the future. 

Whether you’re a student, professional, or enthusiast, these books provide an excellent foundation to kickstart your journey into Artificial Intelligence.

Frequently Asked Questions

What are the Best Artificial Intelligence Books for Beginners?

The best Artificial Intelligence books for beginners include Artificial Intelligence – A Modern Approach by Stuart Russell and Machine Learning for Dummies by John Paul Mueller. These books offer simplified explanations, practical examples, and hands-on exercises tailored for newcomers.

Can I learn AI without a Technical Background?

Yes, books like Machine Learning for Absolute Beginners by Oliver Theobald and Artificial Intelligence Basics by Tom Taulli simplify AI concepts with non-technical explanations and real-world applications. These resources are ideal for readers with minimal or no technical expertise.

How do AI Books Help in Practical Learning?

AI books like Make Your Own Neural Network by Tariq Rashid and Artificial Intelligence By Example by Denis Rothman offer coding exercises and case studies. They provide step-by-step guidance, enabling readers to apply AI concepts and develop hands-on skills effectively.