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Learning Management System (LMS)

Overview

This project aims to build an innovative and engaging Learning Management System (LMS) using Data Science and Machine Learning techniques. The LMS focuses on providing students with a non-boring and interactive learning experience. By integrating advanced machine learning algorithms, the platform ensures personalized and effective learning paths.

Features

  • Interactive Learning Modules: Engaging content tailored to students' needs.
  • Personalized Recommendations: AI-powered suggestions for courses and materials.
  • Progress Tracking: Monitor student performance and provide insights.
  • Data-Driven Insights: Leverage analytics to improve learning outcomes.
  • Scalable Design: Built for growth and seamless integration with new technologies.

Technologies Used

  • Programming Languages: Python, JavaScript
  • Frameworks: Flask/Django, React
  • Machine Learning Libraries: TensorFlow, Scikit-learn
  • Database: PostgreSQL/MySQL
  • Deployment: Docker, AWS/GCP

Installation

  1. Clone the repository: 
    git clone https://github.com/your-username/your-lms-repo.git
cd your-lms-repo
  2. Set up a virtual environment: 
    python -m venv venv
source venv/bin/activate
  3. Install dependencies: 
    pip install -r requirements.txt
  4. Start the development server: 
    python manage.py runserver

Usage

  1. Access the LMS dashboard at http://localhost:8000.
  2. Create an account or log in as an admin to manage courses and users.
  3. Explore the learning modules and track progress.

Contributors

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to the Igniteus team for their support and collaboration on this project.