1.4 KiB
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.