Decision Tree and Random Forest Classification using Julia

4.6
별점
12개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

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

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

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