A 12-week, 26-lesson curriculum covering classic machine learning techniques using Scikit-learn. Designed for beginners, it provides project-based lessons with frequent quizzes while intentionally avoiding deep learning topics.
Project overview
The curriculum provides 52 integrated quizzes and hands-on project building, allowing students to learn classic machine learning techniques step-by-step.
Project type
Data Processing · Learning Resources
Use cases
Data Analysis · Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Educators and general users seeking a structured, project-based 12-week curriculum to learn classic machine learning techniques.
Key capabilities
A 12-week, 26-lesson curriculum covering classic machine learning techniques using primarily Scikit-learn and avoiding deep learning.
Frequent quizzes integrated into the curriculum with 52 total quizzes to help with student retention.
Lessons include step-by-step guides to build projects, allowing students to learn while building.
Many lessons are available in R, provided as R Markdown (.rmd) files in the solution folders.
Automated translations of the curriculum in 50+ languages.
Run the curriculum documentation offline locally using Docsify.
Limitations and risks
The curriculum avoids deep learning topics, which are covered in a separate AI for Beginners curriculum.
Coding is required to complete the lessons and assignments.
Getting started
Clone the repository, install Docsify, run 'docsify serve', and open localhost:3000 to view the documentation locally.
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GitHub project description: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
README: Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called…
README: This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:
README: > **A note about languages**: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the `/solution` folder and look for R…
README: You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine,…