Schedule

Time	Episode Title	Overview
	Setup	Things to complete before starting this lesson.
00:00	1. Introduction	What is a random forest ? How are random forests used ? When might I want to use a random forest ?
00:10	2. Decision Trees	What is a Decision Tree ? What are the major drawbacks of Decision Trees ?
00:20	3. Ensemble Learning	What is an ensemble method ? What is bagging ? What is feature bagging ?
00:30	4. The Random Forest	How is a Random Forest an ensemble method ? How is bootstrap aggregation applied to our decision trees ? How is feature bagging applied to decision tree modeling ?
00:40	5. Training a Random Forest	How do I train a Random Forest in sklearn ? How do I make a prediction on my trained model ? How do I create diagnostic graphs to understand how my model is performing ?
00:50	6. Feature Importance	How can I determine how important a variable is to the model?
00:55	Finish