Introduction to Statistical Learning with Python
Master the fundamentals of machine learning using Python. Based on the ISLP textbook, reimagined for the modern data scientist.
courses.curriculum
The Prologue
Overview of supervised versus unsupervised learning and regression versus classification problems.
Statistical Learning
Basic concepts and theory.
Linear Regression
Focuses on basic concepts of linear statistical learning.
Classification
Covers logistic regression, linear discriminant analysis, and k-nearest neighbors classifier.
Resampling Methods
Details cross-validation and the bootstrap.
Linear Model Selection and Regularization
Explores improvements over standard linear regression, including stepwise selection, ridge regression, the lasso, and principal components regression.
Moving Beyond Linearity
Introduces polynomial regression, splines, and generalized additive models.
Tree-Based Methods
Covers decision trees, bagging, random forests, boosting, and Bayesian Additive Regression Trees (BART).
Support Vector Machines
Explains linear and non-linear classification using SVMs.
Deep Learning
Covers non-linear regression and classification applications.
Survival Analysis and Censored Data
A dedicated chapter on survival analysis techniques.
Unsupervised Learning
Includes clustering methods like K-means and hierarchical clustering, as well as principal components analysis.
Multiple Testing
A chapter on methods for controlling error rates when performing multiple hypothesis tests.