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Welcome to the Hands-on-ML-Basic-to-Advance- wiki!
🚀 Main Wiki Page (Home) Welcome to the ML-CaPsule Wiki! This wiki serves as the central documentation hub for ML-CaPsule, providing essential guides, resources, and references.
📖 Table of Contents: Introduction Getting Started Project Architecture Installation Guide Configuration Usage API Documentation Datasets Contributing FAQ License
ML-CaPsule is a Machine Learning-focused project designed to streamline common ML workflows, from data preprocessing to model deployment. It aims to provide modular, reusable, and efficient ML pipelines. For more details, visit the Repository Home.
Prerequisites: Python >= 3.8, pip, virtualenv Quick Start Guide: Setting up the environment, installing dependencies, and running your first ML pipeline. See More: Getting Started Guide
Overview of the folder structure Explanation of key modules and their purposes Example diagram showcasing the data flow Sample Folder Structure:
Copy code ML-CaPsule/ ├── data/ ├── src/ │ ├── preprocessing/ │ ├── training/ │ ├── evaluation/ │ ├── deployment/ ├── models/ ├── notebooks/ └── README.md See More: Architecture Overview
Step 1: Clone the Repository bash Copy code git clone https://github.com/Niketkumardheeryan/ML-CaPsule.git cd ML-CaPsule
Step 2: Create a Virtual Environment bash Copy code python -m venv venv source venv/bin/activate # For Linux/MacOS venv\Scripts\activate # For Windows
Step 3: Install Dependencies bash Copy code pip install -r requirements.txt
Step 4: Run the Application bash Copy code python main.py See More: Installation Guide
🛡️ 5. Configuration Environment Variables Configuration Files (config.yaml, .env) How to customize settings for specific use-cases See More: Configuration Guide
🚦 6. Usage How to run training pipelines How to preprocess datasets How to evaluate models Example Command:
bash Copy code python src/train_model.py --config config.yaml See More: Usage Guide
📑 7. API Documentation API endpoints (if applicable) Sample Requests and Responses Error Handling See More: API Documentation
📊 8. Datasets Overview of supported datasets Dataset formats Instructions on dataset preparation See More: Datasets Documentation
🤝 9. Contributing We welcome contributions! To contribute:
Fork the repository Create a new branch (feature-xyz) Commit your changes Open a pull request Contribution Guidelines: Contributing Guide
❓ 10. FAQ Q: How do I troubleshoot installation issues? Q: What are the supported Python versions? Q: Where can I report bugs? See More: FAQ Page
📄 11. License MIT License For full license details, see: LICENSE 🛠️ Additional Resources 📚 Official Documentation 🐛 Report Issues 🌟 Star the Repository