Classification Trees in Python, From Start To Finish
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
由 LN 提供2022年5月10日
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.
由 MS 提供2020年5月2日
Good Course. Cost Complexity Pruning explained nicely. Bammmm!!!!!!!!
由 AS 提供2020年6月27日
Liked, easy to understand and utilize the knowledge in a similar dataset.
由 KD 提供2020年8月24日
This is a great course. The instructor does a wonderful job of explaining concepts and providing useful code.