# How to Run the Microsoft AI for Beginners Curriculum: 6 Methods Explained

> Explore 6 methods to run the Microsoft AI for Beginners curriculum locally, in the cloud, or in your browser. Get started with AI education today.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-08-28

---

**The Microsoft AI for Beginners curriculum can be executed locally via Conda, inside a container using VS Code Dev Containers, in the cloud through GitHub Codespaces, directly in the browser via Binder, on GPU-enabled platforms like Azure ML or Google Colab, or by serving the optional Vue.js quiz application.**

The `microsoft/AI-For-Beginners` repository contains a comprehensive 12-week course covering neural networks, computer vision, and natural language processing. Whether you need a GPU-enabled local workstation or a zero-installation cloud environment, the repository provides configuration files such as [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) and [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json) to bootstrap your preferred setup immediately.

## Local Environment Setup with Conda

For full-featured local development with native GPU support, install the curriculum dependencies using the Conda environment specification provided in the repository root.

According to the repository's [`AGENTS.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/AGENTS.md), the primary development environment relies on Miniconda to manage Python packages including TensorFlow and PyTorch. The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file at the repository root lists all core dependencies required for the notebook lessons.

Execute the following commands in your terminal:

```bash
git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
conda env create --name ai4beg --file environment.yml
conda activate ai4beg
jupyter lab

```

Once activated, you can launch either `jupyter notebook` or `jupyter lab` to begin executing the lesson notebooks locally. This method provides persistent storage for datasets and models while allowing direct access to local GPU hardware.

## VS Code Dev Container Configuration

Developers using Visual Studio Code can leverage the pre-configured DevContainer definition to spin up an isolated, reproducible environment without manually installing Conda locally.

The configuration is defined in [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json), which specifies a base image with Miniconda pre-installed and includes a `postCreateCommand` that automatically builds the Conda environment upon container initialization.

To use this method:

1. Clone the repository and open the folder in VS Code.
2. Run the **"Reopen in Container"** command from the command palette.
3. Wait for the `postCreateCommand` to complete the environment setup.
4. Launch Jupyter from the integrated terminal inside the container.

This approach ensures dependency consistency across Windows, macOS, and Linux hosts by containerizing the entire toolchain.

## GitHub Codespaces Cloud Development

GitHub Codespaces provides the same containerized environment as the VS Code Dev Container method, but hosts the compute resources in the cloud, eliminating the need for local Docker installation.

Because Codespaces utilizes the identical [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json) definition found in the repository, the setup process mirrors the local container experience. To launch:

- Navigate to the repository page on GitHub.
- Click **Code → Codespaces → Create new codespace**.
- Once the instance initializes, run `jupyter lab` in the terminal panel.

This option requires only a web browser and GitHub account, making it ideal for students with limited local computing resources or those using Chromebooks.

## Browser-Only Execution via Binder

For immediate access without any local installation or account creation, the curriculum supports MyBinder, a free service that builds and runs Docker images directly from the repository.

Binder uses the separate [`binder/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/binder/environment.yml) file to construct the runtime image. To launch the curriculum in your browser:

1. Click the Binder badge in the [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) at the repository root, or
2. Navigate directly to `https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD`.

Once the image builds, you can select any notebook from the file tree and execute cells immediately. Note that Binder sessions are ephemeral; any data or models created during the session will be lost when the browser tab closes.

## GPU-Accelerated Training on Azure ML or Google Colab

While the standard Conda environment supports CPU execution, the deep learning lessons in the AI for Beginners curriculum benefit significantly from GPU acceleration. For GPU-enabled training, upload individual `.ipynb` files to either **Azure Machine Learning** notebooks or **Google Colab**.

According to the README, you can connect a GPU-enabled compute instance in Azure ML or select a GPU runtime in Colab (Runtime → Change runtime type → GPU), then open any lesson notebook. This method is particularly recommended for training convolutional neural networks in the computer vision modules, where local CPU execution would be prohibitively slow.

## Running the Vue Quiz Application

Beyond the Jupyter notebooks, the repository includes an optional assessment tool built with Vue 2. Located in `etc/quiz-app/`, this application provides interactive quizzes to test your comprehension of AI concepts.

To serve the quiz application locally:

```bash
cd etc/quiz-app
npm install
npm run serve

```

The application will be available at `http://localhost:8080` by default. This component is entirely optional and does not require the Python environment used for the main curriculum.

## Summary

- **Local Conda**: Best for persistent GPU-accelerated development using the root [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml).
- **VS Code Dev Container**: Ideal for consistent, isolated environments without polluting your host system.
- **GitHub Codespaces**: Perfect for cloud-based development with zero local setup beyond a browser.
- **Binder**: Suitable for quick demos or when you cannot install software locally.
- **Azure ML/Colab**: Required for GPU-intensive training when local hardware is insufficient.
- **Quiz App**: Optional Vue.js application in `etc/quiz-app/` for self-assessment via Node.js.

## Frequently Asked Questions

### What is the easiest way to start the AI for Beginners curriculum without installing Python?

**Binder** is the fastest option. Click the Binder badge in the repository [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) or visit the MyBinder URL to launch the notebooks immediately in your browser without downloading any software or creating accounts.

### Can I use the curriculum on an Apple Silicon Mac (M1/M2)?

Yes. The Conda environment specified in [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) supports Apple Silicon architectures. When using the **VS Code Dev Container** method, ensure your Docker Desktop is configured for Apple Silicon, or use **GitHub Codespaces** to bypass local architecture considerations entirely.

### How do I enable GPU support for the neural network lessons?

For local execution, ensure your Conda environment has access to CUDA drivers and install the GPU-enabled variants of TensorFlow or PyTorch. Alternatively, upload the notebooks to **Azure Machine Learning** or **Google Colab** and select a GPU compute target, as documented in the repository's GPU support notes.

### What is the difference between the root [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) and [`binder/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/binder/environment.yml)?

The root [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) is the primary specification for local and DevContainer installations, containing the full development environment. The [`binder/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/binder/environment.yml) is a separate, often lighter, specification used exclusively by MyBinder to build the browser-based sandbox environment.