Introduction to Applied Tree-based Models

Summary

Learn how to use tree-based models to solve complex supervised learning problems.

Description

In this course, you’ll learn how to solve complex supervised learning problems using tree-based models. First, we’ll explain how decision trees can be used to identify complex relationships in data. Then, we’ll show you how to develop a random forest model to build upon decision trees and improve model generalization. Finally, we’ll introduce you to various techniques that you can use to account for class imbalances in a dataset. Throughout the course, you’ll have the opportunity to practice concepts learned in hands-on labs.

Learning objectives

  • Describe how decision trees are used to solve supervised learning problems.

  • Identify complex relationships in data using decision trees.

  • Develop a random forest model to build upon decision trees and improve model generalization.

  • Employ common techniques to account for class imbalances in a dataset.

Prerequisites

  • Intermediate level knowledge about machine learning/machine learning workflows (feature engineering and selection, applying tree-based models, etc.)

  • We recommend that you take the following courses prior to taking this course: Fundamentals of Machine Learning, Introduction to Feature Engineering and Selection with Databricks, Applied Unsupervised Learning with Databricks.

Learning path

  • This course is part of the Data Scientist learning path.

Proof of completion

  • Upon 80% completion of this course, you will receive a proof of completion.