intro-to-data-science/README.md

# 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](http://campus-for-scm.de), 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](https://pipenv.readthedocs.io/en/latest/).

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](intro_to_machine_learning.ipynb).

## Read-only Version

For those interested in just reading the example codes without installing
anything, just open this [notebook](intro_to_machine_learning.ipynb) and view
the Jupyter notebook in your browser.