How to Install AI-For-Beginners: Complete Setup Guide for Microsoft's AI Curriculum

Clone the repository, create the Conda environment from environment.yml, activate it, and launch Jupyter Notebook to start Microsoft's 12-week AI curriculum immediately.

The AI-For-Beginners repository by Microsoft is a comprehensive, open-source curriculum covering artificial intelligence fundamentals through hands-on Jupyter notebooks and a Vue.js quiz application. To install AI-For-Beginners locally, you will configure a Python environment with specific deep learning libraries, optionally deploy the interactive quiz frontend, and verify that lesson notebooks execute correctly. This guide references the exact file paths and commands defined in the microsoft/AI-For-Beginners source code to ensure a reproducible setup.

Step 1: Clone the AI-For-Beginners Repository

Retrieve the source code using Git. The repository contains lesson notebooks under lessons/, example scripts, and a Vue.js quiz application under etc/quiz-app/.

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

If you want to reduce download size by excluding the 50+ language translation directories, use a sparse checkout as documented in the repository's README.md:

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'

Step 2: Create and Activate the Conda Environment

The repository declares all Python dependencies in environment.yml, including pinned versions of NumPy, Matplotlib, OpenCV, PyTorch, TorchVision, and Scikit-learn. Creating the Conda environment ensures that every notebook runs identically across platforms.

conda env create -f environment.yml
conda activate ai4beg

The environment name ai4beg is defined in the environment.yml file. This step installs the exact package versions required by the curriculum, preventing "module not found" errors when executing neural network or computer vision examples.

Step 3: Launch Jupyter Notebooks

With the environment activated, start the Jupyter server to access the interactive lessons. The notebooks are located in the lessons/ directory and automatically download required datasets (e.g., MNIST) to a local data/ folder on first execution.

jupyter notebook

Alternatively, use JupyterLab:

jupyter lab

Open your browser to http://localhost:8888 and navigate to the specific lesson .ipynb files. Each lesson includes both PyTorch and TensorFlow implementations where applicable, and all models train on CPU by default, though GPU acceleration is recommended for later deep learning modules.

Step 4: Run the Optional Quiz Application

The curriculum includes a Vue.js 2.x quiz application located in etc/quiz-app/ that provides interactive knowledge checks for each lesson. This component requires Node.js (≥12) and npm.

cd etc/quiz-app
npm install
npm run serve

The development server starts at http://localhost:8080 with hot-reload enabled. The quiz data resides in etc/quiz-app/src/assets/translations/ and requires no external API keys to function locally. For production deployment to Azure Static Web Apps, refer to the CI/CD workflow documented in etc/quiz-app/README.md.

Alternative: Install Using Development Containers

For a reproducible, Docker-based setup without manual Conda configuration, use the VS Code Dev Container defined in .devcontainer/Dockerfile. Open the repository in Visual Studio Code and select "Reopen in Container". This builds an image with the Conda environment pre-installed and automatically activates the ai4beg environment upon startup.

This method is documented in lessons/0-course-setup/how-to-run.md and provides a consistent Linux-based development environment regardless of your host operating system. GitHub Codespaces users can also leverage this configuration for cloud-based development.

Summary

• Clone the microsoft/AI-For-Beginners repository using standard Git or a sparse checkout to exclude translations and reduce disk usage.
• Create the Conda environment using conda env create -f environment.yml to install pinned versions of PyTorch, OpenCV, and scientific Python libraries.
• Activate the environment with conda activate ai4beg before running any code, as specified in lessons/0-course-setup/setup.md.
• Launch Jupyter Notebook or JupyterLab to execute the curriculum's .ipynb files in the lessons/ directory.
• Optionally serve the Vue.js quiz app from etc/quiz-app/ using npm run serve for interactive lesson reviews.
• Alternatively, use the .devcontainer/Dockerfile configuration for a containerized development environment via VS Code or GitHub Codespaces.

Frequently Asked Questions

Do I need a GPU to run the AI-For-Beginners notebooks?

No. According to the source code in lessons/0-course-setup/setup.md, all notebooks default to CPU execution and will train models successfully on standard hardware. However, GPU acceleration is highly recommended for later lessons involving deep learning to significantly reduce training time for complex neural networks.

Can I install AI-For-Beginners without using Conda?

While the officially supported method uses Conda as defined in environment.yml, advanced users can manually install the packages listed in that file using pip and the requirements.txt reference. Note that the curriculum pins specific versions (e.g., numpy==1.26, matplotlib==3.9) to ensure compatibility, so exact version matching is essential to avoid runtime errors.

How do I update the environment if the repository adds new dependencies?

If the upstream repository updates environment.yml, run conda env update -f environment.yml while the ai4beg environment is activated. This command synchronizes your local installation with any new package additions or version changes committed to the curriculum.

What if I only want the English content without translations?

Use the sparse checkout method documented in the installation steps. The command git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' excludes all translation directories, reducing the repository size dramatically while preserving all English-language lessons, notebooks, and the quiz application referenced in etc/quiz-app/package.json.

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