Decision Trees

Learn how to construct and interpret decision trees.

In this course, you'll learn about entropy, information gain, building decision trees, ID3 algorithm and using random forests to make predictions in Python.

By the end of this course, you'll be able to:

  • Understand the types of relationships in the data that decision trees can represent.
  • Implement the random forests machine learning model.
  • Build a decision tree implementation from the ground up.

Course Info:

Decision Trees


The average completion time for this course is 10-hours.

This course requires a premium subscription. This course has four paid missions and one guided project.  It is the 22nd course in the Data Scientist in Python path.


Learn about Decision Trees

Introduction to Decision Trees

Learn about the building blocks of decision trees, including entropy, and information gain.

Building A Decision Tree

Learn how to create a decision tree using the ID3 algorithm.

Applying Decision Trees

Learn how to apply and tweak decision trees.

Introduction To Random Forests

Learn how to construct and apply random forests.

Predicting Bike Rentals

Apply decision trees and random forests to predict the number of bike rentals.