7 Artificial Intelligence Books to Read in 2025

Artificial intelligence (AI) continues to reshape industries, enhance human intelligence, and push the boundaries of innovation. Whether you are a beginner or an experienced AI professional, staying updated with the best books on artificial intelligence is essential for understanding AI concepts, implementing AI techniques, and applying AI in various fields. Below are seven must-read books on AI and machine learning fundamentals, covering topics like deep learning, reinforcement learning, and AI development.


1. Artificial Intelligence: A Modern Approach – Stuart Russell and Peter Norvig

This classic AI textbook by Stuart Russell and Peter Norvig is widely used in AI courses worldwide. It provides a comprehensive guide to AI, covering topics like:

  • Machine learning fundamentals
  • Deep learning models
  • Reinforcement learning
  • AI systems and algorithms
  • Ethical considerations in AI

Why Read It?

  • Suitable for beginners and advanced AI practitioners
  • Covers AI strategies, techniques, and practical applications
  • A great book for anyone embarking on the AI journey

2. Deep Learning – Ian Goodfellow, Yoshua Bengio, and Aaron Courville

For those diving into deep learning, this book is an essential resource. It provides a solid foundation in:

  • Neural networks and deep learning models
  • TensorFlow and Keras for AI development
  • AI-driven applications and real-world use cases
  • Mathematical concepts heavily used in AI research

Why Read It?

  • One of the best books on AI and deep learning
  • Helps AI professionals and researchers implement AI effectively
  • Includes resources to help you get started with deep learning projects

3. Reinforcement Learning: An Introduction – Richard S. Sutton and Andrew G. Barto

Reinforcement learning (RL) is a key area of AI, and this book is the definitive guide to RL concepts. It covers:

  • Learning models and AI techniques
  • Complex AI algorithms for decision-making
  • AI and machine learning applications in robotics and gaming
  • Advanced AI strategies for self-learning systems

Why Read It?

  • A top book for understanding AI in dynamic environments
  • Covers AI implementation in real-world scenarios
  • A must-read for those interested in AI research and projects

4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Aurélien Géron

This book is perfect for AI enthusiasts who want a practical approach to AI. It focuses on:

  • Python programming and AI libraries like TensorFlow and Keras
  • Machine learning algorithms and best practices
  • AI implementation with real-world projects
  • Ethical considerations in AI technology

Why Read It?

  • Due to its simplicity, it’s an essential guide to AI for beginners
  • A step-by-step book to help you start learning AI
  • Covers topics like AI tools like ChatGPT and other AI-driven applications

5. Pattern Recognition and Machine Learning – Christopher M. Bishop

This book dives into the mathematical foundations of AI and machine learning fundamentals, focusing on:

  • Bayesian networks and probability theory
  • AI concepts applied in computer vision and natural language processing (NLP)
  • Implementing AI with modern statistical approaches
  • AI techniques for pattern recognition

Why Read It?

  • A must-have for AI practitioners and researchers
  • Helps in understanding AI at a deeper level
  • Covers complex AI models and real-world applications of AI

6. AI Superpowers: China, Silicon Valley, and the New World Order – Kai-Fu Lee

For those interested in AI strategies and AI implementation specifically for business, this book is a great read. It discusses:

  • The advancement of AI within major tech industries
  • The role of AI technology in shaping economies
  • Ethical considerations and the future of AI
  • Applications of AI in various industries

Why Read It?

  • Helps AI professionals understand AI’s impact on global economies
  • Covers AI-driven business models and AI strategies for success
  • A must-read for those interested in AI conferences and AI research

7. The Hundred-Page Machine Learning Book – Andriy Burkov

If you are looking for a concise AI guide, this book is perfect. It covers:

  • AI learning journey in an easy-to-read format
  • A beginner-friendly approach to AI concepts
  • Python libraries and popular AI frameworks
  • Applying AI in real-world scenarios

Why Read It?

  • A quick yet comprehensive AI course for those new to AI
  • Covers different AI techniques and best practices
  • Helps readers join AI communities and start applying AI in projects

Final Thoughts

These seven books provide valuable insights into AI, machine learning, and deep learning. Whether you are looking to understand AI at a fundamental level or explore advanced AI topics like artificial general intelligence, reinforcement learning, and generative AI, these books will help you get familiar with AI concepts and AI tools.

For those new to AI, starting with beginner-friendly books like The Hundred-Page Machine Learning Book is a good step. More advanced readers can dive into Artificial Intelligence: A Modern Approach or Deep Learning for a more technical understanding of AI and its applications.

Reading research papers, following AI blogs, and participating in AI conferences can also enhance your AI learning journey. AI technology is constantly evolving, and staying updated with the latest books on AI will keep you ahead in the AI community.