This book provides a comprehensive coverage of machine learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.