What Are the Prerequisites for Using AI-For-Beginners?

To use AI-For-Beginners, you need Python 3.8+, either a full Conda environment created from the repository's environment.yml file, or minimal pip packages for standalone examples, plus Git to clone the repository.

The Microsoft AI-For-Beginners repository delivers a comprehensive 12-week curriculum covering neural networks, computer vision, and natural language processing. Before accessing the interactive Jupyter notebooks and hands-on labs, you must satisfy specific prerequisites for using AI-For-Beginners including proper Python versioning and dependency management.

Core Technical Requirements

Python Version Support

All scripts and notebooks in the curriculum require Python 3.8 or newer. This baseline ensures compatibility with the deep learning frameworks and scientific computing libraries used throughout the course.

Conda Environment Configuration

The recommended setup uses the environment.yml file located in the repository root. This specification pins exact versions of all required libraries, including Jupyter, NumPy, Matplotlib, Scikit-Learn, OpenCV, PyTorch (with TorchVision, TorchText, and TorchData), and TensorFlow-compatible packages.

Creating this environment prevents version conflicts between the various AI frameworks.

conda env create -f environment.yml
conda activate ai4beg

Minimal Pip Installation

For standalone examples only, you can install minimal dependencies documented in examples/README.md. This approach requires only essential packages without the full Conda stack.

For Python scripts:

pip install numpy

For Jupyter notebooks:

pip install jupyter numpy pillow tensorflow

Optional Hardware and Infrastructure

GPU Acceleration

While not required, a GPU significantly accelerates training in deep learning lessons covering computer vision and natural language processing. The notebooks automatically detect GPU availability and provide CPU-only fallback when CUDA is unavailable.

Git and Repository Management

You need Git to clone the repository. To save disk space, perform a sparse checkout excluding the translations folder:

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'

Network Requirements

An internet connection is required for the initial notebook execution to download datasets and pre-trained models. Subsequent runs work offline.

Step-by-Step Environment Setup

Follow this complete workflow to configure your system:

1. Clone the repository (sparse checkout recommended):

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'

2. Create the Conda environment:

conda env create -f environment.yml
conda activate ai4beg

3. Launch a lesson:

jupyter notebook lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb

Or run a standalone example:

python examples/01-hello-ai-world.py

Essential Configuration Files

Understanding these key files ensures proper environment setup:

• environment.yml: The canonical Conda specification at the repository root defining all curriculum dependencies with exact versions.
• examples/README.md: Documents minimal prerequisites for standalone examples, including specific pip commands for basic Python scripts and notebooks.
• lessons/0-course-setup/how-to-run.md: Step-by-step guide for launching notebooks, configuring VS Code or Codespaces, and troubleshooting common issues.
• lessons/0-course-setup/setup.md: Overview of curriculum flow with links to detailed setup instructions.

Summary

• Python 3.8+ is mandatory for all curriculum components.
• Conda environment setup via environment.yml provides the most reliable dependency management for the full 12-week course.
• Minimal pip installation works for standalone examples in the examples/ directory.
• GPU support is optional but recommended for deep learning lessons.
• Sparse checkout saves disk space by excluding translation files.

Frequently Asked Questions

Do I need a GPU to complete the AI-For-Beginners curriculum?

No. While a GPU accelerates training for computer vision and NLP lessons, all notebooks include CPU-only fallback code. You can complete the entire curriculum using CPU-only resources, though training times will be longer for deep learning models.

Can I run the lessons without installing Conda?

Yes. If you prefer not to use Conda, install the minimal pip packages listed in examples/README.md: numpy for Python scripts, and jupyter numpy pillow tensorflow for notebooks. However, the Conda environment ensures exact version compatibility across all 12 weeks.

What if I only want to run specific lessons, not the entire curriculum?

You can perform a sparse checkout to download only specific lesson folders, or simply install the minimal pip requirements and run individual notebooks. The environment.yml approach is recommended if you plan to explore multiple modules, as it includes all dependencies preemptively.

Will the notebooks work offline after the first run?

Yes. After the initial execution downloads datasets and pre-trained models, all lesson notebooks function without an internet connection. Ensure you run each notebook at least once while online to cache the required data locally.

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