This introductory-level course in supervised learning covers regression and classification techniques, including linear and polynomial regression, logistic regression, cross-validation, and more. It also touches on some unsupervised learning methods like principal components and clustering. The course emphasizes practical understanding without heavy math, using R for computing and providing detailed tutorials on its usage. Explore modern data analysis essentials with us.
Prerequisites: Foundational knowledge in statistics, linear algebra, and computing is recommended.
Instructors: Learn from Trevor Hastie, Professor of Statistics at Stanford University, and Robert Tibshirani, Professor in Health Research and Policy and Statistics at Stanford University.
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