Stanford University offers a comprehensive course in Machine Learning that covers the theoretical and practical aspects of this rapidly growing field. Students will learn about various algorithms and models used in ML, including supervised and unsupervised learning, deep learning, and reinforcement learning. The course includes hands-on programming assignments, as well as theoretical concepts such as bias-variance tradeoff, regularization, and optimization. This course is designed for students with a strong background in mathematics and programming who want to develop their expertise in ML.
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