microsoft/ml-for-beginners

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Mindmap

mindmap
  root((repo))
    What it does
      12-week curriculum
      26 lessons total
      Classic ML focus
      Project-based learning
    Tech stack
      Jupyter Notebooks
      Scikit-learn
      Python
      R lessons included
    Use cases
      Learn ML fundamentals
      Teach ML to others
      Build with real data
      Prepare for deep learning
    Learning format
      Interactive notebooks
      Quizzes and assignments
      Multi-language support
      Discussion boards

mindmap root((repo)) What it does 12-week curriculum 26 lessons total Classic ML focus Project-based learning Tech stack Jupyter Notebooks Scikit-learn Python R lessons included Use cases Learn ML fundamentals Teach ML to others Build with real data Prepare for deep learning Learning format Interactive notebooks Quizzes and assignments Multi-language support Discussion boards

Things people build with this

USE CASE 1

Learn machine learning fundamentals if you have basic programming skills and want a structured, free entry point.

USE CASE 2

Teach machine learning to students or colleagues using ready-made lessons, quizzes, and assignments.

USE CASE 3

Build small ML projects on real-world datasets from different cultures to understand how algorithms work in practice.

USE CASE 4

Prepare yourself for advanced topics like deep learning after mastering classic ML techniques.

Tech stack

PythonJupyter NotebookScikit-learnR

Getting it running

Difficulty · easy Time to first run · 5min

Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

This is a free 12-week curriculum from Microsoft that teaches the basics of machine learning to absolute beginners. Machine learning is the area of software where programs find patterns in data instead of being told the answer step-by-step. The course focuses on what it calls classic machine learning, meaning the foundational techniques that came before the deep learning systems used in today's chatbots and image generators. It is built around the Scikit-learn library and explicitly avoids deep learning, which Microsoft covers in a separate AI for Beginners course. The material is structured as 26 lessons spread across 12 weeks, with 52 quizzes plus written instructions, a solution, and an assignment for each lesson. The teaching approach is project-based, so learners build small examples while applying techniques to real-world data drawn from different cultures around the world. Pre- and post-lesson quizzes plus a discussion board reinforce what was just covered. The repository ships as Jupyter Notebooks, an interactive document format that mixes runnable code with explanations, and the course also includes some R lessons. Translations into more than fifty languages are maintained automatically. Someone would use this if they have basic programming familiarity and want a structured, free, beginner-friendly entry point into machine learning before tackling deep learning, or if they are a teacher looking for ready-made coursework. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

I'm new to machine learning and have basic Python skills. Walk me through the first lesson of this Microsoft ML for Beginners course and explain what classic machine learning is.

Prompt 2

Show me how to use Scikit-learn to build a simple classification model using one of the datasets from this course.

Prompt 3

I want to teach machine learning to my team. How can I adapt these Jupyter Notebooks and quizzes for a 4-week workshop?

Prompt 4

Explain the difference between classic machine learning (what this course teaches) and deep learning (what chatbots use).

Prompt 5

Help me set up and run the Jupyter Notebooks from this course on my local machine.

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