MLLearner 0.1.2 - GitHub Packages Ready

What's New in 0.1.2

✅ GitHub Packages Registry Support

This release adds support for publishing directly to GitHub Packages Registry!

Features

• GitHub Packages Registry publishing workflow added
• Automatic publication on release creation
• Dual distribution: PyPI + GitHub Packages

Installation

From PyPI:

pip install mllearner

From GitHub Packages Registry:

pip install --index-url https://pypi.pkg.github.com/HarshTambade/ml-learning-hub mllearner

The package is now available in the GitHub Packages section of this repository!

MLLearner 0.1.1 - GitHub Packages Support

What's New in 0.1.1

Features

• ✅ GitHub Packages Registry Support: Package now publishes to both PyPI and GitHub Packages Registry
• ✅ Dual Distribution: Available on PyPI and GitHub Packages for maximum accessibility
• ✅ Improved Workflow: Updated CI/CD pipeline with GitHub Packages publishing

Bug Fixes

• Fixed workflow configuration for GitHub Packages integration
• Improved authentication handling in automated publishing

Installation

From PyPI:

pip install mllearner

From GitHub Packages:

pip install --index-url https://pypi.pkg.github.com/HarshTambade/ml-learning-hub mllearner

What is MLLearner?

MLLearner is a comprehensive Python package for machine learning education with:

• Data preprocessing and handling utilities
• Classic ML algorithm implementations (Linear Regression, Logistic Regression, KNN, Decision Trees)
• Training utilities and callbacks
• Comprehensive evaluation metrics
• Visualization tools for ML insights

Documentation

For complete documentation, visit: https://github.com/HarshTambade/ML-Learning-Hub

Support

This release marks our expansion to GitHub Packages Registry, making MLLearner accessible through multiple channels!

MLLearner 0.1.0 - Initial Release

MLLearner 0.1.0 - Initial Release

Overview

MLLearner is a comprehensive, educational Python package designed to help machine learning learners and practitioners understand and implement ML algorithms from scratch.

Features

📊 Data Handling Module

• DataLoader: Load CSV and JSON files
• DataProcessor: Handle missing values, encoding, normalization, standardization
• TrainTestSplit: Split datasets into train and test sets

🤖 ML Models

• Linear Regression: Gradient descent-based regression
• Logistic Regression: Binary classification with sigmoid activation
• K-Nearest Neighbors: KNN classifier with Euclidean distance
• Decision Trees: Tree-based classifier with Gini impurity

📈 Evaluation Metrics

• ClassificationMetrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix
• RegressionMetrics: MSE, RMSE, MAE, R² Score

🎓 Training Utilities

• Trainer: Base trainer for model training
• EarlyStoppingCallback: Prevent overfitting
• LearningRateScheduler: Adaptive learning rate scheduling

📊 Visualization Tools

• PlotUtils: Visualization utilities for metrics and results

🔧 Utility Functions

• ModelSaver: Save and load models
• DataValidator: Data validation
• HyperparameterTuner: Grid search for hyperparameters

Installation

pip install mllearner

Quick Start

from mllearner.data import DataLoader, DataProcessor, TrainTestSplit
from mllearner.models import LinearRegression
from mllearner.evaluation import RegressionMetrics

# Load and preprocess data
loader = DataLoader()
data = loader.load_csv('data.csv')

processor = DataProcessor()
X = processor.normalize(X)
y = processor.normalize(y)

# Split data
X_train, X_test, y_train, y_test = TrainTestSplit.split(X, y, test_size=0.2)

# Train model
model = LinearRegression(learning_rate=0.01, iterations=1000)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
rmse = RegressionMetrics.rmse(y_test, y_pred)
print(f"RMSE: {rmse:.4f}")

Documentation

Full documentation available at: https://github.com/HarshTambade/ML-Learning-Hub

License

MIT License

Author

Harsh Tambade