Where to Find AI For Beginners Documentation: Complete Guide to Microsoft's Free AI Curriculum

The documentation for Microsoft's AI For Beginners is built into the repository itself as a Docsify-powered website, with the main entry point at README.md and an interactive web version hosted on GitHub Pages.

The AI For Beginners repository from Microsoft is a self‑contained, 24‑lesson curriculum designed to teach artificial intelligence fundamentals without prerequisites. All documentation lives inside the repository under a clear hierarchy of Markdown files, Jupyter notebooks, and configuration scripts. According to the Microsoft/AI‑For‑Beginners source code, learners can access materials through the root README, a searchable Docsify site, or individual lesson folders.

Accessing the Main Documentation Entry Points

Root README.md

The primary documentation for AI For Beginners starts at [README.md](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md). This file contains:

• A high‑level curriculum summary with badge links
• A "Content" table mapping all 24 lessons to their folder locations
• Quick‑start instructions for new learners
• Links to community Discord and discussion forums

The README follows standard open‑source conventions and renders automatically on GitHub.

Docsify-Powered Website (index.html)

Microsoft provides an interactive, searchable web interface through [index.html](https://github.com/microsoft/AI-For-Beginners/blob/main/index.html). The Docsify configuration includes:

window.$docsify = {
  name: 'AI For Beginners',
  repo: 'https://github.com/microsoft/AI-For-Beginners',
  loadSidebar: true,
  routerMode: 'history'  // Enables clean URLs without hashes
};

To view this locally:

# Install Docsify CLI globally

npm install -g docsify-cli

# Serve the documentation

docsify serve .

The live version is hosted at https://microsoft.github.io/AI-For-Beginners/.

Navigating Lesson-Specific Documentation

Each lesson follows a consistent structure inside lessons/<topic>/. For example, the Perceptron lesson at lessons/3-NeuralNetworks/03-Perceptron/ contains:

File	Purpose
README.md	Theory explanation and learning objectives
Perceptron.ipynb	PyTorch implementation notebook
Perceptron-tf.ipynb	TensorFlow/Keras implementation
lab/ (optional)	Hands-on exercise instructions

You can open any lesson notebook programmatically:

import nbformat as nbf

# Load the Perceptron notebook

path = "lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb"
with open(path, "r", encoding="utf-8") as f:
    notebook = nbf.read(f, as_version=4)

print(f"Cells: {len(notebook['cells'])}")

Setup and Environment Documentation

The AI For Beginners documentation includes detailed environment setup in two key locations:

environment.yml — Conda specification with pinned dependencies:

name: ai4beg
dependencies:
  - python=3.10
  - tensorflow=2.17
  - pytorch
  - keras=3.5
  - opencv
  - numpy=1.26

lessons/0-course-setup/setup.md — Step-by-step guides for:

• Local Conda installation
• GitHub Codespaces
• Binder cloud notebooks
• VS Code dev-container configuration

To create the environment:

conda env create -f environment.yml
conda activate ai4beg

Quick-Start Examples Without Full Curriculum

The [examples/README.md](https://github.com/microsoft/AI-For-Beginners/blob/main/examples/README.md) provides four standalone scripts for immediate experimentation:

# Minimal "Hello AI" verification

python examples/01-hello-ai-world.py

# Simple neural network from scratch

python examples/02-simple-neural-network.py

# Image classifier (Jupyter notebook)

jupyter notebook examples/03-image-classifier.ipynb

# Text sentiment analysis

python examples/04-text-sentiment.py

These examples do not require navigating the full lesson hierarchy.

Quiz Application Documentation

The Vue.js quiz application has its own documentation at etc/quiz-app/README.md:

cd etc/quiz-app
npm install      # Installs Vue 2 and ESLint

npm run serve    # Dev server at localhost:8080

Translations and Visual Assets

Multilingual support and learning aids are organized under:

• translations/* — Localized README files
• lessons/sketchnotes/* — Hand-drawn concept illustrations for each lesson

Contributing and Community Documentation

Community participation guidelines are documented in:

• [CONTRIBUTING.md](https://github.com/microsoft/AI-For-Beginners/blob/main/CONTRIBUTING.md) — Pull request etiquette and issue reporting
• [README.md](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) — Discord server and discussion forum links

Summary

• Primary entry point: README.md in the repository root
• Interactive web version: Docsify site via index.html or GitHub Pages
• Lesson materials: Structured under lessons/<topic>/ with theory READMEs and dual-framework notebooks
• Environment setup: environment.yml and lessons/0-course-setup/setup.md
• Quick examples: examples/ folder for standalone experimentation
• Quiz app: etc/quiz-app/README.md for the Vue.js assessment tool

---

Frequently Asked Questions

Where is the official AI For Beginners documentation hosted?

The official documentation is hosted on GitHub Pages at https://microsoft.github.io/AI-For-Beginners/. This renders the same Markdown content found in the repository's README.md and lesson folders through a Docsify interface.

Can I read the AI For Beginners documentation without installing anything?

Yes. All documentation is readable directly on GitHub by browsing the Markdown files. The README, lesson folders, and setup guides render natively in GitHub's interface without local installation.

How do I find documentation for a specific lesson in AI For Beginners?

Each lesson has a README.md inside its folder under lessons/<topic>/<lesson-number>-<name>/. The root README contains a "Content" table that maps lesson numbers to these paths. For example, lesson 3-3 (Perceptron) is at lessons/3-NeuralNetworks/03-Perceptron/README.md.

What documentation exists for running AI For Beginners in the cloud?

The lessons/0-course-setup/setup.md file documents cloud options including GitHub Codespaces (one-click launch), Binder (free Jupyter environment), and Azure Machine Learning. The repository includes .devcontainer configuration for consistent cloud development environments.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}