Artificial Intelligence and machine learning by hamma shabnam Nasim, Md Riyaz Ansari

Artificial Intelligence (AI) and Machine Learning (ML) represent one of the most transformative technological revolutions of the twenty-first century. From intelligent virtual assistants and autonomous vehicles to predictive analytics in healthcare, finance, and cybersecurity, AI and ML are reshaping the way we live, work, and interact with the world. These technologies are no longer confined to research laboratories; they are now integral to modern industry, governance, and everyday life.

This multi-author volume, Artificial Intelligence and Machine Learning, has been developed to provide a comprehensive, structured, and application-oriented understanding of the subject. The collaborative effort of contributing authors—each bringing expertise from academia, research, and industry—has enriched the content with diverse insights while maintaining conceptual clarity and academic rigor.

The primary objective of this book is to bridge the gap between theoretical foundations and real-world implementation. The text begins with fundamental concepts such as intelligent agents, problem-solving strategies, probability theory, and mathematical foundations. It then progresses to core machine learning paradigms including supervised learning, unsupervised learning, reinforcement learning, and deep learning. Advanced topics such as neural networks, natural language processing, computer vision, model evaluation, optimization techniques, and ethical AI are presented in a systematic and accessible manner.

Special emphasis has been placed on:

Clear explanation of algorithms and mathematical intuition

Step-by-step derivations and illustrative examples

Practical implementation approaches

Case studies and real-world applications

Hands-on problem-solving techniques

Recognizing that AI and ML are inherently interdisciplinary, this book integrates concepts from mathematics, statistics, computer science, and data analytics. The contributing authors have worked collectively to ensure consistency in presentation while preserving the depth required for serious academic study. Numerous solved examples, exercises, and conceptual questions have been incorporated to strengthen analytical and programming skills.

The book is intended for undergraduate and postgraduate students in Computer Science, Information Technology, Electronics, Data Science, and related disciplines. It will also serve as a valuable reference for researchers, professionals, and enthusiasts seeking to understand or implement AI-driven solutions.

As a multi-author collaboration, this work reflects a shared commitment to advancing technical education and fostering innovation. We hope that readers will not only gain a solid foundation in Artificial Intelligence and Machine Learning but also develop the confidence to explore emerging domains such as explainable AI, edge intelligence, federated learning, and responsible AI systems.

We sincerely believe that AI and ML will continue to redefine the boundaries of possibility. It is our aspiration that this book inspires readers to contribute thoughtfully and ethically to this rapidly evolving field.

— The Authors