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

> Visualize the microsoft AI-For-Beginners repository structure with 4 methods: tree utility, Python script, Mermaid diagrams, or VS Code Explorer. Understand your project.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-07-03

---

**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`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) and [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/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:

```bash

# 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:

```bash
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`](https://github.com/microsoft/AI-For-Beginners/blob/main/repo-structure.txt) for easy sharing.

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

```bash
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`](https://github.com/microsoft/AI-For-Beginners/blob/main/generate_tree.py) in the repository root:

```python
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:

```bash
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:

```mermaid
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`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md)** – Contains the high-level overview, lesson table, and quick-start instructions located at the repository root.
- **[`AGENTS.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/AGENTS.md)** – Provides detailed curriculum descriptions and setup guidelines for the 12-week course.
- **[`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml)** – Defines the Conda environment with TensorFlow, PyTorch, and other ML dependencies.
- **[`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/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`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) and [`AGENTS.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/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`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml), [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/requirements.txt), and [`AGENTS.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/AGENTS.md) reside at the root level alongside the main [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/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`](https://github.com/microsoft/AI-For-Beginners/blob/main/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`](https://github.com/microsoft/AI-For-Beginners/blob/main/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`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) (for Conda environments) and [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/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.