This repository contains a collection of various machine-learning models implemented for a range of tasks. These models demonstrate different machine-learning techniques and algorithms used for tasks such as classification, regression, clustering, and more. The code is written in Python, utilizing popular libraries like TensorFlow, Keras, sci-kit-learn, and others.
In this repository, you will find implementations of machine learning models designed to showcase core concepts and techniques. Each model is built to solve specific problems and can be used as a starting point for various machine-learning projects. Whether you are looking to explore supervised or unsupervised learning or need a reference for specific algorithms, this repository aims to provide useful resources for machine learning practitioners.
The models in this repository are modular and easy to modify, making it simple to adapt them to your specific needs. They are designed to be accessible to beginners and helpful for those who are learning or working with machine learning.
- A variety of machine learning models built with different algorithms
- Includes both supervised and unsupervised learning techniques
- Easy-to-follow code, with comments and explanations
- Fully documented to facilitate learning and usage
To use or test the models in this repository, clone the repository and install the necessary dependencies.