# How to Set Up the AI For Beginners Project Locally: Complete Setup Guide

> Set up the AI For Beginners project locally by cloning the repo, creating a Conda environment, and launching Jupyter. Complete the AI curriculum on your machine with this easy guide.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-08-01

---

**Clone the Microsoft AI For Beginners repository, create a Conda environment using the provided [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml) file, and launch Jupyter to execute the curriculum notebooks on your local machine.**

The Microsoft AI For Beginners curriculum provides a comprehensive introduction to artificial intelligence through interactive Jupyter notebooks and hands-on labs. To set up the AI For Beginners project locally, you will configure a Python environment that includes **PyTorch** or **TensorFlow** along with all necessary dependencies for computer vision and NLP lessons. This guide walks through the exact commands and configuration files used in the `microsoft/AI-For-Beginners` repository to get you running in minutes.

## Clone the Repository and Prepare Your Workspace

The curriculum source code lives in the `microsoft/AI-For-Beginners` GitHub repository, which contains lesson notebooks, data download scripts, and an optional Vue.js quiz application.

### Full Clone vs. Sparse Checkout

While a standard clone retrieves the entire repository, the project includes over 50 language translation folders that significantly increase download size. According to the [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) (lines 31-40), you can use a **sparse checkout** to skip these assets if you only need the English curriculum.

For a full clone:

```bash
git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners

```

To exclude translation folders and reduce download size:

```bash
git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

```

## Configure the Python Environment

The repository includes a pre-defined Conda environment specification that installs the exact package versions tested by the curriculum authors.

### Create the Conda Environment

Navigate to the repository root and create the environment using the specification in [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml). This file defines Python, NumPy, Matplotlib, OpenCV, PyTorch, and additional packages required for every lesson.

```bash
conda env create --name ai4beg --file .devcontainer/environment.yml

```

### Activate the Environment

Activate the isolated environment to ensure you are using the correct interpreter:

```bash
conda activate ai4beg

```

## Launch Jupyter and Start Learning

With the environment active, start a local Jupyter server to access the lesson notebooks (`*.ipynb`) located throughout the `lessons/` directory.

### Start Jupyter Notebook or Lab

Choose between the classic interface or the modern JupyterLab:

```bash
jupyter notebook

```

Or for the modern interface:

```bash
jupyter lab

```

Your browser will open to the Jupyter file browser, where you can navigate to specific lessons. For additional execution guidance, refer to [`lessons/0-course-setup/how-to-run.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/how-to-run.md) within the repository.

## Alternative Local Setup Methods

Beyond the standard Conda workflow, the repository supports several alternative development environments.

### Visual Studio Code Integration

Open the repository folder in VS Code:

```bash
code .

```

The Python extension automatically detects the `ai4beg` Conda environment and allows you to run notebooks directly within the editor without launching a separate browser window.

### Docker Dev Container

The `.devcontainer` folder contains a complete container definition. If you have Docker and the VS Code Remote-Containers extension installed, VS Code can build and attach to a pre-configured image containing all curriculum dependencies.

### Cloud Execution Options

If you prefer not to install locally, the curriculum runs identically in GitHub Codespaces, Binder, Azure Data Science VMs, Azure ML notebooks, or Google Colab. These platforms mount the repository and provide the same notebook execution environment without local setup.

## Summary

- **Clone strategically**: Use sparse checkout to exclude translation folders and reduce download size by referencing the instructions in [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) (lines 31-40).
- **Use the provided environment**: The [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml) file ensures you install compatible versions of PyTorch, TensorFlow, NumPy, and other dependencies.
- **Activate before running**: Always run `conda activate ai4beg` before starting Jupyter to ensure the correct Python interpreter is active.
- **Multiple entry points**: Launch via `jupyter notebook`, `jupyter lab`, VS Code, or Docker depending on your workflow preferences.

## Frequently Asked Questions

### What is the exact Conda command to create the AI For Beginners environment?

Run `conda env create --name ai4beg --file .devcontainer/environment.yml` from the repository root. This command reads the environment specification provided by Microsoft and installs Python 3.x, PyTorch, TensorFlow, NumPy, Matplotlib, OpenCV, and all other packages required for the computer vision and NLP lessons.

### Can I run the curriculum without installing Conda or Python locally?

Yes. The repository supports cloud-native execution through GitHub Codespaces, Binder, Azure Data Science VMs, Azure ML notebooks, and Google Colab. These platforms provide pre-configured environments that mirror the local setup, allowing you to execute notebooks directly in your browser without downloading the repository or managing dependencies.

### How do I avoid downloading the translation files when cloning?

Perform a sparse checkout using the commands detailed in [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) (lines 31-40). After running `git clone --filter=blob:none --sparse`, execute `git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'` to exclude the 50+ language translation folders while retaining all lesson content and data files.

### Why does VS Code not detect my Conda environment automatically?

Ensure you have activated the environment at least once using `conda activate ai4beg` before opening VS Code. The Python extension scans for Conda environments on startup; if detection fails, press `Ctrl+Shift+P` (or `Cmd+Shift+P` on macOS), type "Python: Select Interpreter", and manually choose the `ai4beg` environment from the list.