Practical Machine Learning

Model Evaluation

• 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)

Supplementary Videos

• 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

• Complete notes for Practical Machine Learning
• Week 4: Combining Predictors – Math Explained

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.