- Simple Guide to Confusion Matrix Terminology (sensitivity, specificity, etc.)
- ROC curves and Area Under the Curve explained: video tutorial, companion blog post (with video transcript and screenshots)
- What is machine learning, and how does it work?: A high-level overview of machine learning in a 10-minute video
- Video lectures from “An Introduction to Statistical Learning”: Videos for Chapters 4, 5, 6, 8, and 10 can help to deepen your understanding of the topics presented in this course.
Machine Learning Competitions
- Participating in Kaggle’s Allstate Purchase Prediction Challenge: Description of what it’s like to compete in a Kaggle competition, including links to a project paper, R code, presentation slides, and a presentation video.
Choosing a Machine Learning Model
- Comparing Supervised Learning Algorithms: Comparing 8 common supervised learning algorithms (for regression and classification) on 13 different dimensions.
Content Related to the Lectures
Configuring Github Pages with RStudio for PML Project
- Step by step instructions to Configure Github Pages with RStudio to support the PML course project.
Improving Runtime Performance of Caret
- Step by step instructions to implement parallel processing in caret::train() on a random forest model, along with runtime performance analysis for a variety of laptops, ranging from an Intel Atom-based tablet to a quad-core i7 processor.