This book lays the principled foundations of machine learning; it gives readers a detailed view of theoretical concepts and mathematical frameworks that bind the creation of intelligent systems. The approach has not been toward seeing machine learning as just a tool set that creates predictive models but rather toward treating machine learning as a science, with the same level of rigor in experimentation and hypothesis testing followed by validation. It narrows the distance between the practical application of machine learning techniques and deep scientific inquiry, which drives all kinds of innovations. By providing insight into the inner workings of algorithms, data structures, and statistical methods, it gives readers the capability to appreciate critically the performance and limitations of machine learning models. The book also emphasizes reproducibility and transparency in machine learning research, a scientific mindset inspired by open sharing down to the level of data, methods, and results. It depicts machine learning as the effort of a community that flourishes with broad levels of experience and interdisciplinary approaches, with plenty of room for innovation through shared knowledge and collaborative problem-solving. This book is meant for beginner, intermediate, research scholar and data scientist. This book includes case studies, solved numerical and MCQ at the end of each chapter. This book also helps in developing problem-solving approach.

Preface / Acknowledgement / Machine Learning: Introduction / Fundamentals of AI / Python Basics / Python Data Structure / Dimensionality Reduction Techniques / Supervised Learning Algorithms / Clustering / Neural Network / Decision Tree / Support Vector Machine / Instance Based Learning / Reinforcement Learning / Learning Set of Rules / Evaluation.

Undergraduate and Postgraduate Students, Professional and Researchers