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Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to "self-learn" from training data and improve over time, without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions. This book provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed.Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming.
