https://github.com/a-mhamdi/mlpy

Machine Learning with Python
https://github.com/a-mhamdi/mlpy

docker-image keras-tensorflow machine-learning python3 sklearn

Last synced: about 2 months ago
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Machine Learning with Python

• Host: GitHub
• URL: https://github.com/a-mhamdi/mlpy
• Owner: a-mhamdi
• License: mit
• Created: 2022-09-16T20:51:10.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-06T20:21:27.000Z (2 months ago)
• Last Synced: 2024-11-06T21:25:49.360Z (2 months ago)
• Topics: docker-image, keras-tensorflow, machine-learning, python3, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 39.6 MB
• Stars: 9
• Watchers: 4
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning with Python

[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&skip_quickstart=true&machine=standardLinux32gb&repo=537615866&devcontainer_path=.devcontainer%2Fdevcontainer.json&geo=EuropeWest)

[![MLPY-CI](https://github.com/a-mhamdi/mlpy/actions/workflows/mlpy.yml/badge.svg)](https://github.com/a-mhamdi/mlpy/actions/workflows/mlpy.yml)

[![Docker Version](https://img.shields.io/docker/v/abmhamdi/mlpy?sort=semver)](https://hub.docker.com/r/abmhamdi/mlpy)

This repository contains slides, labs, and code samples for using `Python` to implement some **machine learning** related algorithms. 

## Included Algorithms

The repository includes the implementation of the following algorithms:

>1. Linear Regression

>1. Logistic Regression

>1. k-NN

>1. K-MEANS

>1. ANN

## Prerequisites

Codes run on top of a `Docker` image, ensuring a consistent and reproducible environment. 

 You will need to have `Docker` installed on your machine. You can download it from the [Docker website](https://hub.docker.com).

To run the code, you will need to first pull the `Docker` image by running the following command:

```zsh

docker pull abmhamdi/mlpy

```

This may take a while, as it will download and install all necessary dependencies.

## How to control the containers:

* ```docker-compose up -d``` starts the container in detached mode

* ```docker-compose down``` stops and destroys the container

Services can be run by typing the command `docker-compose up`. This will start the `Jupyter Lab` on [http://localhost:2468](http://localhost:2468) and you should be able to use `Python` from within the notebook by starting a new `Python` notebook. You can parallelly start `Marimo` on [http://localhost:1357](http://localhost:1357).

## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.