# Recommended Tools for AI-For-Beginners: Complete Environment Setup

> Set up your AI for beginners environment with recommended tools like Conda Python Jupyter Notebooks TensorFlow and PyTorch. Get started with AI development today.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: getting-started
- Published: 2026-08-11

---

**The AI-For-Beginners curriculum recommends Conda for environment management, Python 3 with Jupyter Notebooks for development, and TensorFlow/PyTorch for deep learning, with optional Azure cloud services for advanced projects.**

Microsoft’s AI-For-Beginners repository is designed as a hands-on learning curriculum that requires specific tools to create a reproducible development environment. Whether you are running the course locally or in the cloud, understanding the recommended tools ensures you can execute every notebook and example without configuration issues.

## Environment Management with Conda

The repository uses **Conda** as its primary package and environment manager to ensure consistent dependency resolution across operating systems.

### Creating the Python Environment

The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file in the repository root defines the complete Python 3 environment, specifying channels like `conda-forge` and listing all required packages. To set up the environment locally, clone the repository and create the environment:

```bash

# Clone with sparse checkout to skip translations (optional)

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'

# Create and activate the environment

conda env create -f environment.yml
conda activate ai4beg

```

The environment file pulls in both Conda and pip packages, including the core scientific stack and deep learning frameworks. A supplementary [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/requirements.txt) file handles additional pip-only dependencies.

## Interactive Development Tools

### Jupyter Notebook and JupyterLab

All lessons in the AI-For-Beginners curriculum are delivered as **Jupyter Notebooks**, making JupyterLab the primary interface for learning. The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) explicitly installs `jupyter`, `ipykernel`, and related packages. Launch the environment with:

```bash
jupyter lab

```

This opens the notebook interface where you can explore the curriculum files located in the `lessons/` directory.

### VS Code Integration

**Visual Studio Code** is the recommended editor for working with the repository, particularly for editing notebooks and running the quiz application. VS Code’s native Jupyter support allows you to execute notebooks directly within the editor, and the repository includes a [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json) configuration for containerized development.

## Deep Learning Frameworks

The curriculum teaches concepts using both major deep learning frameworks to ensure flexibility:

- **TensorFlow** (with Keras as the high-level API)
- **PyTorch**

Each lesson typically provides implementations in both frameworks, allowing you to compare approaches. These are specified in the [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) and installed automatically when you create the Conda environment.

## Computer Vision and Data Science Libraries

For computer vision lessons, the repository includes **OpenCV** (installed via `conda-forge`). The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) also specifies the standard data science stack:

- **NumPy** and **SciPy** for numerical computation
- **scikit-learn** for machine learning utilities
- **matplotlib** for visualization
- **pandas** for data manipulation

These libraries are referenced throughout the notebooks in the `lessons/` directory and example scripts in the `examples/` folder.

## Web-Based and Container Development

### GitHub Codespaces and Devcontainer

For users who prefer not to install dependencies locally, the repository provides a **Devcontainer** configuration in [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json). This Docker-based setup works with GitHub Codespaces or local VS Code with the Remote-Containers extension, creating an isolated development environment with all tools pre-installed.

### Binder for Browser Execution

The README includes a "Launch Binder" badge that connects to **mybinder.org**, allowing you to run the Jupyter notebooks directly in your browser without any local installation. This is ideal for quickly previewing the curriculum or working on machines where you cannot install Conda.

## Interactive Quiz Application

The repository includes a Vue.js-based quiz application located in `etc/quiz-app/`. Built with **Vue 2**, this interactive UI tests your knowledge as you progress through lessons. To run the quiz app locally:

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

```

This starts a development server at `http://localhost:8080`, serving the quiz interface.

## Optional Azure Cloud Services

While not required for the core curriculum, the repository suggests **Azure Cognitive Services**, **Azure Machine Learning**, and **Azure Static Web Apps** for extending projects into production. These services are referenced in the "Helpful Links" and "Getting Started" sections for learners ready to deploy models or build cloud-based AI applications.

## Summary

- **Conda** manages the Python environment via [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml), installing Python 3, Jupyter, TensorFlow, PyTorch, and OpenCV.
- **JupyterLab** serves as the primary interface for executing notebook-based lessons.
- **VS Code** with Devcontainer support offers an alternative local development experience.
- **Binder** provides zero-installation web access to the curriculum.
- **Vue.js** powers the optional quiz application in `etc/quiz-app/`.
- **Azure services** offer optional cloud deployment paths for advanced projects.

## Frequently Asked Questions

### Do I need to install both TensorFlow and PyTorch to complete the course?

No, you can choose either framework. The curriculum provides parallel implementations for most lessons, allowing you to focus on TensorFlow/Keras or PyTorch based on your preference or project requirements. Both are included in the [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) for convenience.

### Can I run AI-For-Beginners without installing Conda?

Yes, you have two alternatives. You can use the **Binder** link in the README to run notebooks in your browser, or open the repository in **GitHub Codespaces**, which uses the Devcontainer configuration to provide a pre-built environment without local installation.

### What Python version does AI-For-Beginners require?

The curriculum requires **Python 3** (specifically the version pinned in [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml), typically 3.8 or higher). The Conda environment file ensures you have a compatible Python version regardless of your system’s default installation.

### How do I access the quiz application?

Navigate to the `etc/quiz-app/` directory after cloning the repository. Ensure you have **Node.js** installed, then run `npm install` followed by `npm run serve` to start the Vue.js development server locally. This is separate from the Python/Jupyter environment.