https://github.com/selcia25/machine_learning_algorithms
👩💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.
https://github.com/selcia25/machine_learning_algorithms
bagging-ensemble boosting classification kmeans-clustering linear-regression multilayer-perceptron-network naive-bayes-classifier random-forest-classifier regression-models
Last synced: 8 months ago
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👩💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.
- Host: GitHub
- URL: https://github.com/selcia25/machine_learning_algorithms
- Owner: selcia25
- License: mit
- Created: 2024-05-11T12:29:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-26T19:58:55.000Z (over 1 year ago)
- Last Synced: 2025-01-02T08:14:37.898Z (10 months ago)
- Topics: bagging-ensemble, boosting, classification, kmeans-clustering, linear-regression, multilayer-perceptron-network, naive-bayes-classifier, random-forest-classifier, regression-models
- Language: Jupyter Notebook
- Homepage:
- Size: 30.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine Learning Algorithms Repository
Welcome to the Machine Learning Algorithms Repository! This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.
## Table of Contents
1. [Description](#description)
2. [Folder Structure](#folder-structure)
3. [Usage](#usage)
4. [List of Algorithms](#list-of-algorithms)
5. [Contributing](#contributing)
6. [License](#license)
## Description
This repository hosts implementations of popular machine learning algorithms, each designed for different tasks such as classification, regression, clustering, and more. The algorithms are implemented in Python using libraries like scikit-learn and TensorFlow.
## Folder Structure
- `Boosting`: Implementation of boosting algorithms.
- `Linear Regression`: Implementation of linear regression algorithms (Note: Deleted directory).
- `Multi Layer Perceptron`: Implementation of neural networks with multiple layers.
- `Naive Bayes`: Implementation of Naive Bayes classifiers.
- `Random Forest Classifier`: Implementation of Random Forest classifiers.
- `model`: Directory containing various machine learning models and notebooks.
## Usage
You can explore the directories to find implementations of different machine learning algorithms. Each directory typically contains Python scripts or Jupyter Notebook files demonstrating how to use the algorithm with example datasets. Simply run the scripts or notebooks to see the algorithms in action.
## List of Algorithms
- Boosting algorithms
- Multi-layer Perceptron (Neural Networks)
- Naive Bayes classifiers
- Random Forest classifiers
- Other machine learning models
## Contributing
If you'd like to contribute to this repository by adding more machine learning algorithms or improving existing ones, feel free to fork the repository, make your changes, and submit a pull request. Contributions are always welcome!
## License
This repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.