ml-core

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About

ml-core is an open-source repository under the HashSlap Summer of Code initiative, dedicated to implementing core Machine Learning projects across various domains. It's beginner-friendly, contribution-ready, and built entirely in Python.

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⭐️ Project Structure

Each folder in this repository represents a subdomain of machine learning. Contributors can choose issues or create projects under:

ml-core/
├── computer-vision/
├── natural-language-processing/
├── supervised-learning/
├── unsupervised-learning/
├── deep-learning/
├── time-series/
├── generative-models/
└── reinforcement-learning/

Each project should include:

• Well-structured code
• A README.md explaining the approach
• Sample results/outputs

Preview to Sections:

• Computer Vision
• NLP
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Neural Networks
• Data Analysis

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⭐️ How to Contribute

We welcome first-time contributors and experienced ML practitioners alike!

Steps to Contribute

• Fork the repository - Click the Fork button at the top-right corner of this repository to create your own copy.

• Pick an Issue or Propose Your Own Project - Check the Issues tab or open a new issue describing your idea or improvement.

• Clone the repository

  git clone https://github.com/<your-username>/<repo-name>.git
  cd <repo-name>

• Create a new branch

  git checkout -b <your-branch-name>

• Create your project in the relevant domain folder.

  mkdir <domain>/<your_project_name>

• Add your code + README

• Stage and Commit your changes.

  git add .
  git commit -m "Added my project on <topic>"

• Push to your created branch

  git push origin <your-branch-name>

• Submit a Pull Request (PR)

  Go to your forked repository on GitHub → Click on Compare & pull request → Describe your changes clearly → Submit!

  📌 Tip: Before pushing, always pull the latest changes to avoid merge conflicts:

  git pull origin main --rebase

Contributions will be reviewed and merged by mentors.

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⭐️ Experiment Logger

Located at: utils/experiment_logger.py

☆ Features

• Logs training results (accuracy, loss, hyperparameters, etc.) to CSV
• Supports multiple experiments for comparison
• Visualize performance trends using matplotlib/seaborn

☆ Requirements

pip install matplotlib seaborn pandas

☆ How to Use

from utils.experiment_logger import log_experiment, plot_metrics

☆ Log an experiment

log_experiment( model_name='Perceptron', metrics={'accuracy': 0.9, 'loss': 0.12}, hyperparams={'learning_rate': 0.01, 'epochs': 20}, dataset_name='Iris' )

☆ Plot results

plot_metrics(metric='accuracy')

☆ Integration

Just import and call log_experiment(...) at the end of your model training script. It works across all subdomains like supervised-learning/, deep-learning/, etc.

⭐️ Why Contribute?

• Build real ML projects
• Deepen conceptual knowledge
• Boost your GitHub profile
• Get featured in contributor spotlights
• Earn participation certificates and digital badges

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⭐️ Templates

☆ ML Pipeline Template

A reusable and beginner-friendly Python script that demonstrates a typical Machine Learning pipeline.
Supports CLI usage with argparse for flexible input and automation.

📂 Location: templates/ml_pipeline_template.py

☆☆ Features:

• Load data from CSV/JSON
• Preprocess and split into train/test
• Train a simple ML model (e.g., Logistic Regression)
• Evaluate performance
• Easily adaptable for your own datasets

☆☆ How to Run:

python templates/ml_pipeline_template.py --data iris.csv --target species

📜 License

This repository is licensed under the MIT License.

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