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Create Classification Trees in Python
Apply Cost Complexity Pruning in Python
Apply Cross Validation in Python
Create Confusion Matrices in Python
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.
Confusion Matrix
Classification Trees
Cost Complexity Pruning
Cross Validation
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由 RR 提供
Aug 17, 2020Josh Starmer's videos and courses are always simple and easy to understand. Thank you for this wonderful course. I will definitely recommend everyone to take this course.
由 II 提供
Aug 27, 2020Good platform to learn about this type of project.
由 KD 提供
Aug 24, 2020This is a great course. The instructor does a wonderful job of explaining concepts and providing useful code.
由 LN 提供
May 10, 2022The 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.
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