📋 Participating Programs

Name	Logo	Purpose
GSSoC'2024-Extd		The coding period is from October 1st to October 30th, during which contributors make contributions and earn points on the platform.
Hacktoberfest 2024		Hacktoberfest is a month-long October event welcoming all skill levels to join the open-source community.

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📊 Project Metrics

🌟 Stars	🍴 Forks	🐛 Issues	🔔 Open PRs	🔕 Closed PRs

ML-Nexus

A dynamic hub of Machine Learning innovations, where hands-on projects and collaborative experiments come together to inspire open-source contributions and foster a community of shared learning.

This repository is a diverse collection of projects ranging from beginner-friendly models to advanced AI applications. Whether you're new to the field or a seasoned expert, there's something for everyone to contribute to. Dive into neural networks, computer vision, natural language processing (NLP), and more. Join our vibrant community, share your ideas, and help shape the future of AI—together!

Join official Discord Channel for discussion

Natural Language Processing (NLP)

Natural Language Processing (NLP) Projects in this area involve working with text data, such as sentiment analysis, language translation, text summarization, and chatbot development using techniques like tokenization, word embeddings, and transformers.

Computer Vision

Computer Vision Contributors can explore projects related to image classification, object detection, facial recognition, and image segmentation using tools like OpenCV, convolutional neural networks (CNNs), and transfer learning.

Neural Networks

Neural Networks Neural networks power most deep learning models. Contributions could include creating models for image classification, regression tasks, sequence prediction, and generative models using frameworks like TensorFlow or PyTorch.

Generative Models

Generative Models This includes working on projects related to Generative Adversarial Networks (GANs) for image generation, text-to-image models, or style transfer, contributing to fields like art creation and synthetic data generation.

Time Series Analysis

Time Series Analysis Contributors can work on analyzing temporal data, building models for stock price prediction, climate forecasting, or IoT sensor data analysis using LSTM or GRU networks.

Transfer Learning

Transfer Learning Explore projects where pre-trained models are fine-tuned for specific tasks, such as custom object detection or domain-specific text classification, reducing the need for extensive training data.

📚 Machine Learning Resources

This project uses a number of key libraries to implement machine learning models and data processing pipelines. To help you better understand these libraries and their roles in the project, we've created a dedicated guide.

For an in-depth overview of the most important libraries used in this project, including their features and functionalities, check out the Machine Learning Libraries Overview.

This guide covers:

• NumPy 🧮 for numerical computations.
• Pandas 📊 for data manipulation.
• TensorFlow 🤖 and PyTorch 🔥 for deep learning.
• And more!

We encourage you to explore this document to gain a deeper understanding of the tools that power our machine learning workflows.

📚 Generative AI resources

To get in-depth overview and roadmap to learn Generative AI. Check out Generative AI Roadmap.

This guide covers:

• Overview of generative AI
• Roadmap to learn Generative AI
• LLM models 🤖
• Retrieval Augumented Generation (RAG)
• Vector and graph databases
• Embedding models
• Inference APIs
• PDF scrapping 🗒️
• AI agents 🤖

📚 Deep Learning Roadmap

To get an in-depth overview and roadmap to learn Deep Learning, check out Deep Learning Roadmap.

This guide covers:

• Overview of deep learning
• Roadmap to learn deep learning
• Types of neural networks 🧠
• Key deep learning concepts
• Regularization techniques 💡
• Model optimization 🔧
• Transfer learning 🚀
• Deep learning applications 📷📝🔊
• Best practices and resources

⭐ How to get started with open source?

You can refer to the following articles on the basics of Git and Github.

• Watch this video to get started, if you have no clue about open source
• Forking a Repo
• Cloning a Repo
• How to create a Pull Request
• Getting started with Git and GitHub
  
  
  
  

Troubleshooting 🛠️

This section addresses some common issues you may encounter while setting up or using ML-Nexus. If you run into problems not listed here, please feel free to consult our community or open a new issue.

1. Installation Issues

• Problem: Error related to dependencies during installation.
  • Solution: Ensure you have the required versions of all dependencies by running:

    pip install -r requirements.txt

  • For missing packages, try installing them individually:

    pip install <missing-package-name>

2. Permission Denied Errors

• Problem: "Permission denied" error while cloning or pushing to the repository (via SSH/HTTPS).
  • Solution: Ensure you have the correct SSH keys set up or use HTTPS. To clone via HTTPS, use:

    git clone https://github.com/UppuluriKalyani/ML-Nexus.git

  • If SSH is preferred, add your SSH key to GitHub using these steps.

3. Outdated Packages

• Problem: Errors caused by outdated packages that are incompatible with ML-Nexus.
  • Solution: Update all packages by running:

    pip install --upgrade -r requirements.txt

  • Alternatively, update individual packages if specified in the error logs:

    pip install --upgrade <package-name>

4. Common Runtime Errors

• Problem: Unexpected errors or crashes when running scripts.
  • Solution: Make sure you’re using the correct Python version (e.g., Python 3.8+). You can check your Python version with:

    python --version

  • If issues persist, try creating a fresh virtual environment:

    python -m venv myenv
    source myenv/bin/activate  # For Windows: myenv\Scripts\activate
    pip install -r requirements.txt

For further assistance, feel free to reach out to our community or check the issues section on GitHub.

💥 How to Contribute to ML-Nexus?

• Take a look at the Existing Issues or create your own Issues!
• Wait for the Issue to be assigned to you.
• Fork the repository

click on the uppermost button

1. Fork the repository to your own GitHub account.

2. Clone the repository to your local machine:

   git clone https://github.com/<your-username>/ML-Nexus.git

3. Navigate into the directory:

   cd ML-Nexus

4. Install dependencies (if applicable):

   npm install

5. Create a new branch for your changes:

   git checkout -b <your-branch-name>

6. Make your changes, commit, and push:

   git add .
   git commit -m "Your message here"
   git push origin <your-branch-name>

7. Submit a pull request:

   • Go to the original repository on GitHub.
   • Click on the "Pull Requests" tab.
   • Click the "New Pull Request" button.
   • Select your feature branch and submit the pull request.

8. Wait for review and feedback.

   • Address any comments or requested changes.
   • Once approved, your feature will be merged into the main branch.

• Have a look at Contributing Guidelines
• Read the Code of Conduct

Contributors👩‍💻👨‍💻

Community and Contributing

Please do! Contributions and pull requests are welcome.Contributors are expected to adhere to the Code of Conduct.

Jump into our Discord!

Code of Conduct🤝

To maintain a safe and inclusive space for everyone to learn and grow, contributors are advised to follow the Code of Conduct.

Feedback📝

We value your feedback! If you have suggestions or encounter any issues, feel free to:

• Open an issue here
• Reach out to the maintainer: Uppuluri Kalyani

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