Fundamental of Machine Learning and Deep Learning

<p><span style=background-color: rgba(245 245 244 1); color: rgba(121 121 121 1)>Machine learning and deep learning have emerged as transformative forces revolutionized industries and driving innovation across diverse domains such as healthcare finance and autonomous systems. Fundamentals of Machine Learning & Deep Learning offers a comprehensive and structured introduction to these dynamic fields catering to both beginners and seasoned professionals seeking to deepen their expertise. The book begins by establishing a strong theoretical foundation with Bayesian Decision Theory and fundamental machine learning concepts gradually progressing to advanced topics such as neural networks ensemble learning and deep learning applications. Each chapter strikes a balance between theoretical depth and practical implementation ensuring readers can seamlessly connect concepts to real-world scenarios. Core topics including classification and regression algorithms component analysis and clustering techniques are presented with clarity and reinforced with illustrative examples. The advanced sections delve into cutting-edge areas such as deep learning optimization techniques convolutional neural networks (CNNs) and hybrid models integrating supervised and unsupervised learning approaches. Whether you are a student researcher or industry professional this book serves as a reliable guide to mastering both foundational principles and advanced methodologies in machine learning and deep learning. By the end readers will have developed a solid grasp of the underlying principles and practical applications ranging from traditional linear models to state-of-the-art deep neural networks. This holistic approach ensures not just conceptual understanding but also the ability to apply knowledge effectively in solving complex real-world challenges. </span></p>