Machine Learning

The primary aim of this book is to provide students, educators, researchers, and aspiring professionals with a strong foundation in machine learning concepts, algorithms, and practical implementation techniques. It is designed to help learners understand how intelligent systems learn from data, recognise patterns, make predictions, and support decision-making in real-world applications. This book aims to: - Introduce the fundamentals and types of machine learning.- Explain core supervised, unsupervised, and reinforcement learning techniques. - Develop analytical thinking for selecting suitable algorithms for different problems. - Provide practical knowledge using Python and popular ML libraries. - Bridge theoretical understanding with hands-on experimentation. - Prepare learners for advanced studies, research, and industry applications in AI and data science.