https://github.com/bzubs/mlzero
Implementation of widely used ML algorithms in vanilla python
https://github.com/bzubs/mlzero
machine-learning machine-learning-algorithms machinelearning numpy python statistics
Last synced: 6 months ago
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Implementation of widely used ML algorithms in vanilla python
- Host: GitHub
- URL: https://github.com/bzubs/mlzero
- Owner: bzubs
- Created: 2025-04-13T10:22:59.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-06-14T11:09:49.000Z (8 months ago)
- Last Synced: 2025-08-19T11:58:38.936Z (6 months ago)
- Topics: machine-learning, machine-learning-algorithms, machinelearning, numpy, python, statistics
- Language: Python
- Homepage:
- Size: 172 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MLZero: Machine Learning from Scratch
[](https://pypi.org/project/mlzero/)
[](https://opensource.org/licenses/MIT)
## Overview
**MLZero** is a Python library providing a collection of machine learning algorithms implemented from scratch. The goal is to offer a clear, educational codebase for understanding the fundamentals of machine learning, with practical driver scripts and a modular design for easy extension.
## Features
- **Classifiers**: Perceptron, AdaLine, Logistic Regression, k-Nearest Neighbors (kNN), Naive Bayes, Softmax Regression
- **Clusterers**: K-Means clustering algorithm
- **Regressors**: Linear regression (closed-form and gradient descent), L1 (Lasso) and L2 (Ridge) regularization, ElasticNet, polynomial regression, multiple variable regression
- **Small Neural Nets**: Basic implementation of a multi-neuron layer
- **Decomposers**: Principal Component Analysis (PCA)
- **Metrics**: Regression and classification metrics (MAE, MSE, R², accuracy, precision, recall, F1, etc.)
## Requirements
To run this project, you need the following Python libraries:
- `numpy`
- `matplotlib` for plotting purpose not necessary otherwise, recommended to have installed
Install the dependencies using:
```bash
pip install -r requirements.txt
```
## Directory Structure
```
mlzero/
├── classifiers/ # Classification algorithms and driversC/
├── clusterers/ # Clustering algorithms and driversK/
├── regressors/ # Regression algorithms and driversR/
├── decomposers/ # Dimensionality reduction and driversD/
├── metrics/ # Regression and classification metrics
├── small_neural_net/ # Multi-neuron layer implementations
└── requirements.txt # Dependencies list
```
## Usage
Each algorithm has a corresponding driver script in its drivers subdirectory. For example:
**Run the ElasticNet regressor:**
```bash
python regressors/driversR/driverElasticNet.py
```
**Run the kNN classifier:**
```bash
python classifiers/driversC/driverKNNClassifier.py
```
## Development Status
MLZero is under active development. The codebase is modular and designed for educational purposes. Contributions for new algorithms, bug fixes, and documentation improvements are welcome.
## Contributing
Contributions are welcome! Feel free to fork the repo, submit issues, or open pull requests. Please ensure your code is well-documented and tested before submitting.
## License
This project is licensed under the MIT License.