Machine Learning course, taught by Andrew Ng, provides a rigorous introduction to core ML concepts. Covering regression, classification, neural networks, SVMs, decision trees, ensemble methods, and reinforcement learning, it emphasizes theory, algorithms, and practical applications. Students gain deep insight into building, debugging, and improving machine learning models.

This course introduces fundamental machine learning concepts, including supervised and unsupervised learning, neural networks, and probabilistic models. Emphasizing both theory and practical applications, it equips students with tools to analyze data, build predictive models, and understand algorithmic trade‑offs, preparing them for advanced study and real‑world problem solving.