This book offers an in-depth exploration of the principles, techniques, and applications of machine learning. Starting with foundational concepts such as data preprocessing and model evaluation, the book covers both supervised learning models like regression and classification, and advanced topics like ensemble learning, neural networks, and deep learning. Practical considerations, including handling imbalanced data, feature engineering, and preventing data leakage, are thoroughly discussed to help build robust models. Designed for students, professionals, and enthusiasts alike, this guide provides valuable insights and practical knowledge to navigate and excel in the field of machine learning.

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