“Handbook on Fundamentals and Methods of Machine and Deep Learning” delves into the core
principles, techniques, and innovations in artificial intelligence. This book explores the key
methodologies that drive machine learning and deep learning, from classical approaches to cutting-edge
neural network models. Divided into comprehensive sections, the book covers supervised, and
reinforcement learning before moving on to delve into deep learning topics, such as convolutional and
recurrent neural networks. Each chapter is meticulously structured to provide a theoretical foundation,
followed by practical insights and real-world applications. Case studies are included to showcase how
machine learning and deep learning methods are transforming sectors such as healthcare, finance, and
natural language processing. Additionally, readers will find resources to help them code algorithms and
build models independently. This handbook is intended for those interested in artificial intelligence, from
beginners seeking foundational knowledge to experts aiming to enhance their understanding. With a
clear, accessible approach, this book strives to be a valuable resource for anyone aspiring to attach the
power of machine and deep learning in a practical, impactful way.