Decision Tree and Random Forest Classification using Julia

4.6
stars
12 ratings
Offered By
Coursera Project Network
In this Guided Project, you will:

Learn about stumps, decision trees and random forests.

Learn how to check the performance of a decision tree and random forest.

Work with a real world dataset.

Clock1 hour 30 minutes
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

This guided project is about glass classification using decision tree classification and random forest classification in Julia. It is ideal for beginners who do not know what decision trees or random forests are because this project explains these concepts in simple terms. While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning. Special features: 1) Simple explanations of important concepts. 2) Use of images to aid in explanation. 3) Challenges to ensure that the learner gets practice. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Decision Tree
  • Data Analysis
  • Random Forest
  • Classification Algorithms
  • julia

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Learn about stumps and their importance.

  2. Learn how to build a decision tree.

  3. Learn how to prune a decision tree.

  4. Learn how to build a random forest.

  5. Learn how to do hyper parameter tuning

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.