🧠 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

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🚀 How to Contribute

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

📦 Steps:

1. Fork the repository 🍴
2. Pick an issue or propose your own project
3. Create a folder inside the relevant domain
4. Add code + README
5. Submit a Pull Request ✅

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`](https://docs.python.org/3/library/argparse.html) 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](LICENSE).

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