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A comprehensive introduction to machine learning covering the mathematical foundations, data preprocessing, supervised and unsupervised learning, model evaluation, feature engineering, neural networks, and practical applications. The course combines academic foundations with hands-on problem solving and advanced topics for students who want to go beyond the standard curriculum.
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