NCSU-Libraries/Introduction-to-Machine-Learning

Name: Introduction-to-Machine-Learning

Owner: NCSU Libraries

Description: Free online Introduction to Machine Learning tutorial on Datacamp.com

Forked from: damilolah/Introduction-to-Machine-Learning

Created: 2017-08-09 18:19:45.0

Updated: 2017-08-18 17:37:20.0

Pushed: 2017-08-21 14:25:16.0

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README

DataCamp Template Course

This is an instructional Resource on Machine Learning brought to you by NCSU Libraries.

This course is available on DataCamp

• Course Instructor: Ruth Okoilu
• University : North Carolina State University
• Difficulty_level : 2
• Time_needed : 2 hours

Changes you make to this GitHub repository are automatically reflected in the linked DataCamp course. This means that you can enjoy all the advantages of version control, collaboration, issue handling … of GitHub.

Workflow

1. Edit the markdown and yml files in this repository. You can
2. Use DataCamp's Teach Editor
3. Use GitHub's online editor
4. Use git locally and push your changes
5. Check out your build attempts on the Dashboard.
6. Check out your automatically updated course on DataCamp

Getting Started

A DataCamp course consists of two types of files:

• course.yml, a YAML-formatted file that's prepopulated with some general course information.
• chapterX.md, a markdown file with:
• a YAML header containing chapter information.
• markdown chunks representing DataCamp Exercises.

To learn more about the structure of a DataCamp course, check out the documentation.

Every DataCamp exercise consists of different parts, read up about them here. A very important part about DataCamp exercises is to provide automated personalized feedback to students. In R, these so-called Submission Correctness Tests (SCTs) are written with the testwhat package. SCTs for Python exercises are coded up with pythonwhat. Check out the GitHub repositories' wiki pages for more information and examples.

Want to learn more? Check out the documentation on teaching at DataCamp.

Happy teaching!