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Machine Learning for Undergraduate Students is a comprehensive guide designed to make the complex world of machine learning accessible to beginners. This book introduces foundational concepts, starting with the need for machine learning, its relationship with other fields, and its diverse applications. Through a structured exploration of essential topics, readers will gain a clear understanding of data analysis, including univariate, bivariate, and multivariate statistics, as well as techniques like feature engineering and dimensionality reduction. Building on these basics, the book delves into core machine learning methodologies. Topics include similarity-based learning, regression analysis, decision tree algorithms, and Bayesian learning. The chapters also introduce artificial neural networks, explaining their biological inspiration, architecture, and applications. Advanced subjects such as clustering algorithms, proximity measures, and reinforcement learning-covering Q-Learning and SARSA-are presented with clarity, ensuring a thorough understanding of each concept. With engaging examples and detailed figures and tables (52 figures and 44 tables), this book provides learners with a strong foundation in machine learning concepts, preparing them to explore opportunities in both academic and professional settings. Designed for undergraduates, career changers, and anyone curious about this rapidly evolving field of machine learning, this textbook serves as a valuable resource for understanding and navigating its complexities.
