A 12-week, 26-lesson project-based curriculum covering classic machine learning techniques using Scikit-learn. It includes quizzes, assignments, and solutions while avoiding deep learning topics.
Project overview
A 12-week, 26-lesson project-based curriculum covering classic machine learning techniques using Scikit-learn. It includes quizzes, assignments, and solutions while avoiding deep learning topics.
Project type
Data Processing
Use cases
Data Analysis · Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Educators seeking a structured, project-based curriculum with integrated quizzes to teach classic machine learning concepts to beginners.
Students and general users who want to learn classic machine learning techniques using Scikit-learn with step-by-step project guides.
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; those topics are covered in a separate AI for Beginners curriculum.
Getting started
Clone the repository and optionally install Docsify for local offline access. The setup difficulty is rated as easy.
Clone the repository, install Docsify, run 'docsify serve', and open localhost:3000 to access the curriculum.
Evidence and sources
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,…