Guide ISLR Chapter 10 - Unsupervised Learning Summary of Chapter 10 of ISLR. In unsupervised learning, we have features, but no response. The goal is not to predict anything. Instead, the goal is to discover subgroups and relationships.

Guide ISLR Chapter 9 - Support Vector Machines Summary of Chapter 9 of ISLR. Support vector machines are one of the best classifiers in the binary class setting.

Guide ISLR Chapter 8 - Tree-Based Methods Summary of Chapter 8 of ISLR. Simple tree-based methods are useful for interpretability. More advanced methods, such as random forests and boosting, greatly improve accuracy, but lose interpretability.

Guide ISLR Chapter 7 - Moving Beyond Linearity Summary of Chapter 7 of ISLR. We can move beyond linearity through methods such as polynomial regression, step functions, splines, local regression, and GAMs.

Guide ISLR Chapter 6 - Linear Model Selection & Regularization Summary of Chapter 6 of ISLR. There are alternative methods to plain least squares, which can result in models with greater accuracy and interpretability.

Guide ISLR Chapter 5 - Resampling Methods Summary of Chapter 5 of ISLR. Resampling methods allow us to choose a model that has the most predictive power.

Guide ISLR Chapter 4 - Classification Summary of Chapter 4 of ISLR. Classification involves predicting qualitative responses. Logistic regression, LDA, and KNN are the most common classifiers.

Guide ISLR Chapter 3 - Linear Regression Summary of Chapter 3 of ISLR. Simple and multiple linear regression are common and easy-to-use regression methods.

Guide ISLR Chapter 2 - What is Statistical Learning? Summary of Chapter 2 of ISLR. Statistical learning refers to a set of approaches for determining what our predictors tell us about our response.

Guide ISLR Chapter 1 - Introduction to Statistical Learning Summary of Chapter 1 of ISLR. Statistical learning simply refers to the broad set of tools that are available for understanding data.