Machine Learning Authors: Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das

Machine Learning by Saikat Dutt, Subramanian Chandramouli, and Amit Kumar Das is a comprehensive textbook that presents the fundamental principles, theoretical underpinnings, and applied techniques in modern machine learning. Published by Pearson, the book is tailored for a broad range of learners—including undergraduate and postgraduate students, data science enthusiasts, and software professionals transitioning into AI.

The book introduces readers to:

Core machine learning concepts and mathematical foundations

Key algorithms for supervised, unsupervised, and reinforcement learning

Practical case studies and examples for real-world implementation

Python-based exercises and solutions to solidify learning

Trends and challenges in deploying machine learning systems

What sets this book apart is its structured approach: starting with a clear conceptual base and progressing to hands-on applications. It bridges the gap between theory and practice, ensuring readers not only understand algorithms but also how to build and evaluate models effectively.

The cover’s imagery—featuring a robotic arm holding a human figure amid digital clouds—symbolizes the intersection of AI and humanity, hinting at the future-shaping potential of intelligent systems.

Whether you're building predictive systems, analyzing big data, or exploring academic research in AI, Machine Learning offers a powerful foundation for mastering the science of intelligent learning.