# How to Set Up the Microsoft AI for Beginners Project Locally Using Conda

> Learn how to set up the Microsoft AI for Beginners project locally using Conda. Follow these simple steps to install Miniconda, clone the repo, create your environment, and launch Jupyter Notebook.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-08-29

---

**To set up the Microsoft AI for Beginners curriculum locally, install Miniconda, clone the repository, create the Conda environment from [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml), activate the `ai4beg` environment, and launch Jupyter Notebook to access the lessons at `http://localhost:8888`.**

The Microsoft **AI for Beginners** repository is an open-source educational curriculum containing Jupyter notebooks, supporting scripts, and a Vue-based quiz application. Setting up the AI for Beginners project locally using Conda ensures all dependencies—TensorFlow, PyTorch, OpenCV, and scikit-learn—are isolated and correctly configured according to the source specifications.

## Prerequisites: Install Miniconda

Before cloning the repository, you need the Conda package manager. The repository recommends **Miniconda**, a lightweight installer that provides isolated Python runtimes without the full Anaconda distribution.

For Linux or macOS, run:

```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"

```

Windows users should download and run the Miniconda installer from the official Conda website.

## Clone the Repository

Navigate to your desired directory and clone the GitHub repository to pull all lesson notebooks, data files, and configuration files:

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

```

## Create and Activate the Conda Environment

The repository defines the Python environment in [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) at the root directory. This YAML file specifies the environment name `ai4beg` and includes core dependencies like `numpy`, `matplotlib`, `opencv`, and `pytorch`, while also pulling additional packages from [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/requirements.txt).

Create the environment by running:

```bash
conda env create -f environment.yml

```

Once created, activate the environment to register the Jupyter kernel and put the installed packages on your `PATH`:

```bash
conda activate ai4beg

```

If you prefer using the VS Code dev-container configuration locally, you can alternatively point to the mirror spec:

```bash
conda env create -f .devcontainer/environment.yml

```

## Launch Jupyter Notebook

According 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), once the environment is activated, you can start Jupyter to serve the curriculum notebooks locally.

Launch the notebook server:

```bash
jupyter notebook

```

Alternatively, use JupyterLab:

```bash
jupyter lab

```

This serves the application at `http://localhost:8888`, allowing you to navigate to any lesson under `lessons/.../*.ipynb` and execute the code cells with all dependencies properly loaded.

## Alternative: VS Code Dev Container Setup

If you prefer containerized development over local Conda installation, the repository includes a `.devcontainer` configuration. Opening the project in Visual Studio Code triggers a prompt to "Reopen in Container," which builds the same `ai4beg` environment inside Docker using [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml). The configuration file [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json) orchestrates this containerized setup, mirroring the dependencies defined in the top-level YAML file.

## Summary

- **Install Miniconda** to obtain the `conda` package manager and isolated Python runtimes
- **Clone the repository** from `https://github.com/microsoft/AI-For-Beginners.git`
- **Create the environment** using `conda env create -f environment.yml` to build the `ai4beg` environment
- **Activate the environment** with `conda activate ai4beg` to access TensorFlow, PyTorch, and other dependencies
- **Launch Jupyter** via `jupyter notebook` to run all curriculum notebooks locally

## Frequently Asked Questions

### What is the exact name of the Conda environment for AI for Beginners?

The environment is named **`ai4beg`** as defined in the `name` field of [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml). You must activate this specific environment using `conda activate ai4beg` before launching Jupyter, as this ensures the kernel has access to all required machine learning libraries listed in the configuration.

### Can I use pip instead of Conda to set up the project?

While the repository includes a [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/requirements.txt) file referenced by [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml), the official documentation in [`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) recommends **Conda** for setup. Conda properly handles binary dependencies like OpenCV and PyTorch across platforms, whereas pip may fail to resolve system-level library requirements correctly.

### What Python packages are included in the ai4beg environment?

The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) bundles major machine learning frameworks including **TensorFlow** and **PyTorch**, computer vision libraries such as **OpenCV**, and data science staples like **NumPy**, **matplotlib**, and **scikit-learn**. Additional packages are installed via `pip` from [`requirements.txt`](https://github.com/microsoft/AI-For-Beginners/blob/main/requirements.txt) during the environment creation process.

### How do I verify the environment is working correctly?

After running `conda activate ai4beg`, launch `jupyter notebook` and open any notebook file (e.g., `lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb`). If the kernel starts without import errors for TensorFlow or PyTorch, your local setup using Conda is functioning correctly.