How to Visualize the AI-For-Beginners Repository Structure: 4 Methods Explained

You can visualize the Microsoft AI-For-Beginners repository structure using the command-line tree utility, a Python script, Mermaid diagrams, or VS Code's Explorer panel.

The microsoft/AI-For-Beginners repository is organized as a single-purpose educational curriculum with a deliberately simple top-level layout. Understanding how to visualize the repository structure helps newcomers quickly locate lessons, notebooks, and supporting assets across the 12-week machine learning course.

Repository Structure Overview

The AI-For-Beginners repo follows a flat hierarchy designed for educational accessibility. At the root level, you will find configuration files like environment.yml and requirements.txt, alongside the core lessons/ directory that contains the entire curriculum.

The complete directory structure appears as follows:

├─ .github/                # GitHub workflow & CI files

├─ .devcontainer/          # VS Code dev-container definition

├─ etc/
│   ├─ quiz-app/           # Vue 2 quiz application (source & build scripts)

│   └─ quiz-src/           # Quiz-generation helpers

├─ lessons/
│   ├─ 0-course-setup/     # Environment-setup guides

│   ├─ 1-Intro/            # Introductory AI concepts

│   ├─ 2-Symbolic/         # Knowledge-representation notebooks

│   ├─ 3-NeuralNetworks/   # Perceptron, MLP, PyTorch/TensorFlow basics

│   ├─ 4-ComputerVision/   # OpenCV, CNNs, GANs, segmentation, etc.

│   ├─ 5-NLP/              # Text-representation, RNNs, Transformers

│   ├─ 6-Other/            # GA, RL, multi-agent systems

│   ├─ 7-Ethics/           # Responsible AI material

│   └─ X-Extras/           # Advanced topics (CLIP, VQ-GAN, …)

├─ translations/           # 40+ language packs (auto-generated)

├─ translated_images/      # Localized image assets

├─ binder/                 # Binder-specific env files

├─ images/                 # General graphics (favicons, etc.)

├─ examples/               # Quick-start notebooks (Hello AI World, etc.)

├─ environment.yml         # Conda environment definition

├─ requirements.txt        # Pip-based Python dependencies

├─ README.md               # Repository overview & lesson table

├─ AGENTS.md               # High-level curriculum & setup description

└─ …                       # Additional docs (LICENSE, CONTRIBUTING, etc.)

Method 1: Command-Line Tree Utility

The fastest way to visualize the repository structure on Unix-like systems is the tree command. This utility prints a hierarchical view of directories and files.

First, install tree if it is not already present:

# Debian/Ubuntu

sudo apt-get install tree

# macOS (Homebrew)

brew install tree

Run the command at the repository root to generate a text-based visualization:

tree -L 2 --charset ascii > repo-structure.txt

• -L 2 limits the depth to two levels (adjust as needed).
• --charset ascii ensures compatibility across different terminals.
• The output redirects to repo-structure.txt for easy sharing.

For a graphical SVG representation, pipe the output to graph-easy:

tree -L 3 -i | graph-easy --as=svg > repo-structure.svg

Method 2: Python Directory Tree Generator

You can generate a custom tree view using a short Python script. This method works cross-platform and requires no external dependencies beyond the standard library.

Create a file named generate_tree.py in the repository root:

import os

def walk(dir_path, prefix=""):
    entries = sorted(os.listdir(dir_path))
    for i, entry in enumerate(entries):
        path = os.path.join(dir_path, entry)
        connector = "└─ " if i == len(entries) - 1 else "├─ "
        print(f"{prefix}{connector}{entry}")
        if os.path.isdir(path):
            extension = "   " if i == len(entries) - 1 else "│  "
            walk(path, prefix + extension)

if __name__ == "__main__":
    root = "."        # repo root

    print(f".")
    walk(root)

Execute the script and redirect the output to a Markdown file:

python generate_tree.py > tree.md

Running this script produces an indented tree identical to the ASCII structure shown above, which you can paste directly into documentation.

Method 3: Mermaid Diagrams for Markdown

Many documentation platforms including GitHub, Docsify, and MkDocs render Mermaid syntax natively. This approach embeds a clickable, navigable diagram directly into your Markdown files.

Insert the following block into any .md file:

graph TD
  A[repo root] --> B[lessons]
  A --> C[etc]
  A --> D[translations]
  B --> B0[0-course-setup]
  B --> B1[1-Intro]
  B --> B2[2-Symbolic]
  B --> B3[3-NeuralNetworks]
  B --> B4[4-ComputerVision]
  B --> B5[5-NLP]
  B --> B6[6-Other]
  B --> B7[7-Ethics]
  C --> C1[quiz-app]
  C --> C2[quiz-src]
  A --> E[environment.yml]
  A --> F[README.md]

View the file on GitHub or any Mermaid-enabled viewer to see an interactive visualization of the microsoft/AI-For-Beginners structure.

Method 4: Visual Studio Code Explorer

For immediate visual feedback without running commands:

1. Open the repository folder in VS Code.
2. Press ⌘⇧E (macOS) or Ctrl+Shift+E (Windows/Linux) to open the Explorer pane.
3. The Explorer displays the identical hierarchy found in the file system, including the lessons/, etc/, and translations/ directories.

For a printable export, install the Export Files Tree extension and select File → Export as PNG to capture the current state of the repository structure.

Key Source Files and Directories

When visualizing the repository, focus on these high-impact entry points:

• README.md – Contains the high-level overview, lesson table, and quick-start instructions located at the repository root.
• AGENTS.md – Provides detailed curriculum descriptions and setup guidelines for the 12-week course.
• environment.yml – Defines the Conda environment with TensorFlow, PyTorch, and other ML dependencies.
• requirements.txt – Lists pip dependencies for Binder deployments and local installations.
• lessons/ – Houses all Jupyter notebooks and markdown lesson content for the core curriculum (subdirectories 0-course-setup/ through X-Extras/).
• etc/quiz-app/ – Contains the Vue 2 quiz application source code and build scripts.
• translations/ – Stores 40+ language packs auto-generated from the source material.
• binder/ – Includes Binder-specific configuration files for instant notebook environments.
• .devcontainer/ – Defines the VS Code development container for reproducible development setups.

Summary

• Use tree -L 2 for quick ASCII hierarchy generation on Linux and macOS.
• Use the Python script for cross-platform tree generation without external dependencies.
• Use Mermaid diagrams to embed interactive visualizations in GitHub-flavored Markdown.
• Use VS Code Explorer for real-time navigation and PNG export of the structure.
• The repository centers on the lessons/ directory, with supporting files like environment.yml and AGENTS.md providing curriculum context.

Frequently Asked Questions

What is the main directory structure of the AI-For-Beginners repository?

The repository organizes content under lessons/ (containing subdirectories like 1-Intro/, 3-NeuralNetworks/, and X-Extras/), etc/ (housing the quiz application), and translations/ (language packs). Configuration files including environment.yml, requirements.txt, and AGENTS.md reside at the root level alongside the main README.md.

How do I visualize the repository structure on Windows without installing tools?

On Windows, use the Python script method provided above. Save the generate_tree.py file in the repository root and run python generate_tree.py in Command Prompt or PowerShell. This requires no additional software beyond Python, which is already needed for the course curriculum.

Can I view the repository structure directly in GitHub?

Yes. GitHub renders the file tree in the web interface when you navigate to the repository. For a more detailed view, open the README.md file, which contains a complete lesson table mapping the lessons/ directory structure, or use the Mermaid diagram method to embed custom visualizations in your own documentation.

What are the key configuration files for setting up the curriculum?

The primary setup files are environment.yml (for Conda environments) and requirements.txt (for pip installations), both located at the repository root. The .devcontainer/ directory contains VS Code container definitions for reproducible development environments, while binder/ provides configuration for launching notebooks in Binder.

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