Powered by:
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.
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.
Make sure that you have Python installed on your machine.
You might want to use venv 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.
Install all dependencies that are required for the project by running:
pip install -r requirements.txtAll demos in the project may be run directly in your browser without installing Jupyter locally. But if you want to launch Jupyter Notebook locally you may do it by running the following command from the root folder of the project:
jupyter notebookAfter this Jupyter Notebook will be accessible by http://localhost:8888.
Each algorithm section contains demo links to Jupyter NBViewer. 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. You may do it by simply clicking the "Execute on Binder" link in top right corner of the NBViewer.
The list of datasets that is being used for Python and Jupyter Notebook demos may be found in tools folder.
Please read contact me.
This project is licensed under the MIT License - see the LICENSE.md file for details