Foundations of Machine Learning and Deep Learning is a comprehensive guide designed to provide
readers with an in-depth understanding of the principles and applications of ML and DL. It covers a
broad spectrum of topics, from the basics of machine learning to advanced architectures and
emerging trends, making it suitable for a diverse audience.
The book is structured into eight chapters, each focusing on critical aspects of the field. Starting with
an introduction to machine learning, it delves into fundamental concepts like data representation,
supervised and unsupervised learning, and neural network fundamentals. The later chapters explore
deep learning architectures such as CNNs, RNNs, and transformers, along with advanced topics like
GANs, autoencoders, and reinforcement learning.
Practicality is a cornerstone of this book. It introduces popular tools and frameworks like TensorFlow
and PyTorch, providing insights into setting up environments, leveraging pretrained models, and
optimizing performance. Real-world applications are interwoven throughout, showcasing how ML
and DL are driving innovation across industries such as healthcare, finance, and autonomous
systems.