Fundamental of Machine Learning and Deep Learning
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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 n...
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.
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