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In the context of a book category, "Supervised Learning" refers to a collection of works that delve into the principles, methodologies, and applications of supervised learning within the field of machine learning and artificial intelligence. These books typically cover a range of topics including the foundational theories of supervised learning, various algorithms such as linear regression, decision trees, and neural networks, as well as practical guidance on implementing these techniques in real-world scenarios. Readers can expect to find both theoretical insights and hands-on examples that illustrate how labeled data is used to train models to make predictions or classify information. This category is ideal for students, researchers, and practitioners seeking to deepen their understanding of how machines can be taught to learn from data under human supervision.

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