Introduction to Machine Learning with Python

General Notes

This project contains a Jupyter notebook introducing some very basic concepts of machine learning and the popular Iris classification case study.

First, some simple linear algebra ideas are shown via examples with the numpy library. Then a so-called K-nearest-neighbor algorithm is trained to classify flower from the Iris dataset.

This intro is aimed at total beginners to programming and machine learning.

It was used within a 90 minute workshop at the WHU Campus for Supply Chain Management, which targets students of business administration and young management professionals.

Installation

This project uses popular Python libraries that can be installed via the pipenv command line tool. To do so, run pipenv install or pipenv install --ignore-pipfile (to use the exact environment as of the time of this writing). For a tutorial on pipenv, go to the official documentation.

After installation, start Jupyter via the command jupyter notebook and wait for a new tab to be opened in your default web browser. Then, open the notebook called intro_to_machine_learning.ipynb.

Read-only Version

For those interested in just reading the example codes without installing anything, just open this notebook and view the Jupyter notebook in your browser.