In recent decades, Artificial Intelligence (AI) and Machine Learning (ML) have transformed from abstract theoretical concepts into essential tools that are reshaping industries, research, and everyday life. From powering search engines and recommendation systems to enabling autonomous vehicles and medical diagnostics, these technologies are rapidly becoming the backbone of modern innovation.
This book is designed to serve as a comprehensive introduction to the foundations and applications of AI and ML. It aims to bridge the gap between theory and practice by combining mathematical rigor with real-world examples. Whether you are a student exploring these topics for the first time, a researcher seeking deeper insights, or a professional aiming to apply intelligent systems in your domain, this book offers a structured path to learning.
The early chapters focus on the fundamental principles of AI—problem solving, knowledge representation, reasoning, and learning. From there, we delve into the core concepts of machine learning, including supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning. We also touch upon ethical considerations and the societal implications of deploying AI systems.
Throughout the book, we have prioritized clarity, context, and engagement. Each chapter is supplemented with examples, exercises, and case studies to encourage critical thinking and hands-on learning. The content is aligned with the latest trends and research, ensuring that readers are well-prepared for both academic and industry pursuits.
We would like to thank the countless researchers, practitioners, and educators whose work has shaped the AI and ML landscape—and whose insights continue to inspire new generations of learners. It is our hope that this book contributes meaningfully to that ongoing journey of discovery.
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