ML - Template

A clean, framework-agnostic template for organizing and starting any Machine Learning project

You can use this template as a starting point for any ML project, regardless of framework (scikit-learn, TensorFlow, PyTorch, etc.).

Project structure

ml-template/
├── data/ # Datasets (raw and processed)
├── experiments/ # To track experiments (results, notes, configs, and saved models)
├── notebooks/ # Jupyter notebooks for exploration and prototyping
├── src/ # Source code (data, models, utils) for reproducible code
├── tests/ # Unit tests for code validation (optional)
├── .gitignore # Files and folders ignored by git
├── LICENSE # License for the repo (MIT)
├── README.md # Repo overview and instructions
└── requirements.txt # Python dependencies 

Information

This repository is a template for starting any Machine Learning project.
It provides a well-structured folder layout and placeholder files to help you organize your project efficiently.

Key points:

• Framework-agnostic: works with scikit-learn, TensorFlow, PyTorch, or any other ML library.
• Ready-to-use structure for data, experiments, notebooks, source code, and tests.
• Designed for both research experiments and production-ready projects.

Use this template as a starting point to create reproducible, clean, and maintainable ML projects.

Getting started

1. Locally

1. Clone the repository:

   git clone https://github.com/thomastschinkel/ml-template.git
   cd ml-template

2. Create a virtual environment

   python -m venv .venv
   source .venv/bin/activate   # Linux/Mac
   .venv\Scripts\activate      # Windows

3. Install dependencies

  pip install -r requirements.txt

You can modify requirements.txt as needed to avoid installing unnecessary dependencies. Currently it only contains the Top 10. ML libraries in Python

2. Cloud

1. Clone the repository:

   !git clone https://github.com/thomastschinkel/ml-template.git
   %cd ml-template

2. Install dependencies

 !pip install -r requirements.txt

Some installations will be skipped as these libraries are often pre-installed on cloud-environments.

Note: You have to run these commands in a notebook cell.

Now you are ready to start your ML-Project.🚀