Course overview

In this course, you’ll learn how to build a decision tree implementation. You’ll learn concepts such as entropy, information gain, an error metric known as Area Under the Curve (AUC), and the ID3 algorithm. You’ll also explore the random forest algorithm and learn to reduce overfitting. Finally, you’ll learn to improve prediction quality using ensemble decision trees.

You’ll use scikit-learn, a machine learning library for Python that simplifies training machine learning models, and you’ll construct both decision trees and random forest algorithms to improve performance and accuracy.

At the end of the course, you’ll complete a project that requires you to apply different machine learning models to predict the number of bike rentals using data from a communal bike sharing station in Washington D.C. This project is a chance for you to combine the skills you learned in this course and practice the machine learning workflow by implementing a decision tree. You can also use this project to show off your machine learning skills to future employers.

Key skills

    • Identifying the data relationships that decision trees can represent
    • Implementing the random forest machine learning model
    • Building a decision tree implementation

Course outline

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Projects in this course

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