Introduction to Statistical Learning Course

Linear Regression

3.1 Simple Linear Regression

3.2 Multiple Linear Regression

3.3 Interaction Terms

3.4 Non-linear Transformations of the Predictors

3.5 Qualitative Predictors

3.6 Writing functions combining modeling and plotting.

Classification

4.1 Logistic Regression

4.2 Linear Discriminant Analysis

4.3 Quadratic Discriminant Analysis

4.4 K-Nearest Neighbors

Cross-Validation and the Bootstrap

5.1 The Validation Set Approach

5.2 Leave-One-Out Cross-Validation

5.3 k-Fold Cross-Validation

5.4 The Bootstrap

Linear Model Selection and Regularization

6.1 Best Subset Selection

6.2 Forward and Backward Step-wise Selection

6.3 Model Selection Using a Validation Set

6.4 Model Selection by Cross-Validation

6.5 Ridge Regression and the Lasso

Moving Beyond Linearity

7.1 Introduction

7.2 Polynomials

7.3 Polynomial logistic regression

7.4 Splines

7.5 Generalized Additive Models

Tree-Based Methods

8.1 Introduction

8.2 Random forests

8.3 Boosting

Support Vector Machines

9.1 Introduction

9.2 Linear Support Vector Classifier

9.3 Non-linear Support Vector Machine.

Unsupervised Learning

10.1 Principal Components

10.2 k-means Clustering

10.3 Hierarchical Clustering

Statement of Accomplishment

Of course, what really matters when learning online is not the certificate but how much one puts in to understanding the material. In this vein, I often referenced The Elements of Statistical Learning to dive deeper into topics of interest. If a topic in ISLR leaves you curious for more, ESLR is an excellent compliment. The Authors have generously made both texts available online.