# How to Run the Examples in AI For Beginners: A Complete Setup Guide > Learn how to run AI For Beginners examples by cloning the repo, creating a Conda environment, and executing Python scripts or Jupyter notebooks. Get your AI setup quickly. - Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) - Tags: how-to-guide - Published: 2026-08-02 --- **Clone the Microsoft AI For Beginners repository, create the Conda environment from [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml), and run Python scripts with `python` or notebooks with `jupyter notebook`.** The **AI For Beginners** repository by Microsoft provides self-contained, beginner-friendly code examples that demonstrate core AI concepts without requiring the full curriculum notebooks. This guide walks you through running these examples step by step, from environment setup to executing your first machine learning model. ## Prerequisites for Running AI For Beginners Examples Before running any examples, ensure you have **Python 3.8 or higher** installed. The repository targets cross-platform compatibility across Windows, macOS, and Linux. Two dependency management approaches are supported: - **Conda environment** (recommended): Uses [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) to pin exact versions for reproducibility - **Minimal pip install**: Lightweight setup for running only the Python scripts The Conda approach automatically registers a Jupyter kernel, enabling direct notebook execution from the repository. ## Creating the Conda Environment The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file in the repository root defines an isolated environment named **ai4beg** with all required libraries: `numpy`, `jupyter`, `tensorflow`, `pillow`, and others. Run these commands from the repository root: ```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 ``` **Alternative lightweight setup** (Python scripts only): ```bash pip install numpy jupyter pillow tensorflow ``` ## Running Python Script Examples The `examples/` directory contains four self-contained scripts, each runnable directly with the standard interpreter. ### Hello AI World Demo The [`01-hello-ai-world.py`](https://github.com/microsoft/AI-For-Beginners/blob/main/01-hello-ai-world.py) script trains a minimal linear model (`y = 2x`) to introduce fundamental training concepts: ```bash python examples/01-hello-ai-world.py ``` **Expected output:** ``` ======================================== Welcome to Hello AI World! ======================================== 📊 Creating training data... Training examples: [(1, 2), (2, 4), (3, 6), (4, 8), (5, 10)] 🎓 Training started... Epoch 20/100 - Average error: 0.4375 - Weight: 1.84 ... ✅ Training complete! Final weight: 1.99 ``` Each script includes a top-level `main()` guard (`if __name__ == "__main__":`) for direct execution. ### Neural Network From Scratch Run the multilayer perceptron implementation: ```bash python examples/02-simple-neural-network.py ``` ### Text Sentiment Analysis Execute the tiny sentiment model on sample sentences: ```bash python examples/04-text-sentiment.py ``` ## Running Jupyter Notebook Examples Some examples require interactive execution. The `03-image-classifier.ipynb` notebook demonstrates image classification with a pre-trained CNN. Launch the notebook server: ```bash jupyter notebook examples/03-image-classifier.ipynb ``` **Execution flow:** 1. First cell: Import TensorFlow and NumPy 2. Second cell: Download and display a sample image 3. Third cell: Load MobileNet, run inference, and print top-5 predictions The notebook interface renders results inline, making it ideal for visualizing model outputs. ## Using VS Code Dev Containers or GitHub Codespaces For zero-configuration execution, the repository includes [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json). This configuration pre-installs the identical Conda environment in a Docker container. **Steps:** 1. Open the repository in VS Code 2. Click **"Reopen in Container"** when prompted 3. The Jupyter server starts automatically This approach eliminates local dependency management entirely. ## Key Files for Running AI For Beginners Examples | File | Purpose | |------|---------| | [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) | Conda specification with pinned Python and library versions | | [`examples/01-hello-ai-world.py`](https://github.com/microsoft/AI-For-Beginners/blob/main/examples/01-hello-ai-world.py) | Linear model trainer—your first "Hello AI" demo | | [`examples/02-simple-neural-network.py`](https://github.com/microsoft/AI-For-Beginners/blob/main/examples/02-simple-neural-network.py) | Feed-forward network implemented from scratch | | `examples/03-image-classifier.ipynb` | Interactive image classification with pre-trained CNN | | [`examples/04-text-sentiment.py`](https://github.com/microsoft/AI-For-Beginners/blob/main/examples/04-text-sentiment.py) | Sentiment analysis on short text inputs | | [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json) | VS Code container definition for one-click setup | ## Summary - **Clone** `microsoft/AI-For-Beginners` to your local machine - **Create environment** using `conda env create` or minimal `pip install` - **Activate** the `ai4beg` environment before running examples - **Run scripts** with `python examples/