https://github.com/alexgenovese/machine-learning-algorithms

All codes are developed during some course. This repo was born in order to catalog different machine learning algorithms, all written in Python, from 0 to advanced solutions.
https://github.com/alexgenovese/machine-learning-algorithms

machine-learning-algorithms machinelearning python3

Last synced: about 20 hours ago
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All codes are developed during some course. This repo was born in order to catalog different machine learning algorithms, all written in Python, from 0 to advanced solutions.

• Host: GitHub
• URL: https://github.com/alexgenovese/machine-learning-algorithms
• Owner: alexgenovese
• License: mit
• Created: 2020-05-01T15:25:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-06-22T01:54:53.000Z (over 2 years ago)
• Last Synced: 2024-04-23T00:09:55.907Z (7 months ago)
• Topics: machine-learning-algorithms, machinelearning, python3
• Language: Python
• Homepage:
• Size: 4.59 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine learning algorithms

*Powered by:*

![Python Logo](https://raw.githubusercontent.com/willtheorangeguy/Python-Logo-Widgets/master/pythonpoweredlengthgif.gif)

> All codes are developed during some course. This repo was born in order to catalog different machine learning algorithms, all written in Python, from 0 to advanced solutions.

## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

### Prerequisites

#### Installing Python

Make sure that you have [Python installed](https://realpython.com/installing-python/) on your machine.

You might want to use [venv](https://docs.python.org/3/library/venv.html) standard Python library

to create virtual environments and have Python, `pip` and all dependent packages to be installed and 

served from the local project directory to avoid messing with system wide packages and their 

versions.

#### Installing Dependencies

Install all dependencies that are required for the project by running:

```bash

pip install -r requirements.txt

```

#### Launching Jupyter Locally

All demos in the project may be run directly in your browser without installing Jupyter locally. But if you want to launch [Jupyter Notebook](http://jupyter.org/) locally you may do it by running the following command from the root folder of the project:

```bash

jupyter notebook

```

After this Jupyter Notebook will be accessible by `http://localhost:8888`.

#### Launching Jupyter Remotely

Each algorithm section contains demo links to [Jupyter NBViewer](http://nbviewer.jupyter.org/). This is fast online previewer for Jupyter notebooks where you may see demo code, charts and data right in your browser without installing anything locally. In case if you want to _change_ the code and _experiment_ with demo notebook you need to launch the notebook in [Binder](https://mybinder.org/). You may do it by simply clicking the _"Execute on Binder"_ link in top right corner of the NBViewer.

## Datasets

The list of datasets that is being used for Python and Jupyter Notebook demos may be found in [tools folder](tools).

## Built With

* [Python 3](https://www.python.org)

## Contributing

Please read contact me.

## License

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