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.
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.
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.
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.
- Boosting algorithms
- Multi-layer Perceptron (Neural Networks)
- Naive Bayes classifiers
- Random Forest classifiers
- Other machine learning models
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!
This repository is licensed under the MIT License. See the LICENSE file for details.