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  Machine learning is an ever-evolving field with a vast array of applications, including image and speech recognition, natural language processing, and predictive analytics. Data scientists, who are at the forefront of this technological revolution, need to stay up-to-date with the latest advancements in machine learning. This can be accomplished by reading the rightbooks. In this article, we present the top 5 machine learning books that every data scientist should have in their library.   1. Pattern Recognition and Machine Learning by Christopher Bishop Pattern Recognition and Machine Learning is considered a classic in the field of machine learning. This book provides a comprehensive overview of machine learning techniques and algorithms, including linear regression, logistic regression, decision trees, support vector machines, and neural networks. The author, Christopher Bishop, is a renowned machine learning expert and a former Microsoft researcher. The book is written i...