Classification Trees in Python, From Start To Finish
9,230명이 이미 등록했습니다.
9,230명이 이미 등록했습니다.
In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices. Notes: - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Cost Complexity Pruning
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
II 제공2020년 8월 27일
Good platform to learn about this type of project.
LN 제공2022년 5월 10일
The instructor has a great teaching style. I have enjoyed his sense of humour throughout the course. All the details are explained clearly and thoroughly by written notes or verbal explanation.
AS 제공2020년 6월 27일
Liked, easy to understand and utilize the knowledge in a similar dataset.
MS 제공2020년 5월 2일
Good Course. Cost Complexity Pruning explained nicely. Bammmm!!!!!!!!