توضیحات
Mathematics of Machine Learning provides a clear and structured introduction to the mathematical principles underlying machine learning algorithms. Topics include linear algebra, calculus, probability, statistics, optimization, and numerical methods, all explained with relevance to ML applications. The book emphasizes understanding the theory behind models such as regression, classification, clustering, and neural networks, enabling readers to analyze, design, and improve algorithms effectively. Designed for students, data scientists, and researchers, it equips readers with the mathematical tools and insights required to develop a deep, rigorous understanding of machine learning and its practical applications.
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