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.
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- Level Professional
- المدة 2 ساعات hours
- الطبع بواسطة Coursera Project Network
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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.الوحدات
Classification Trees in Python, From Start To Finish
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Assignment
- Graded Quiz: Test your Project understanding
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Labs
- Classification Trees in Python, From Start To Finish
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Readings
- Project-based Course Overview
Auto Summary
Dive into building Classification Trees in Python with this hands-on, 1-hour course designed for IT & Computer Science enthusiasts. Guided by an expert instructor, you'll tackle real-world datasets, employ One-Hot Encoding, and apply Cost Complexity Pruning and Cross Validation. Hosted on Coursera's interactive Rhyme platform, you'll have seamless access to pre-configured cloud desktops with all necessary tools. Ideal for those with Python knowledge and an understanding of Decision Trees, this intermediate-level course is free and most effective for learners in North America.
Josh Starmer