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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.