From b3e1a761e838b95eeb97fc3e6441d383f12ec4f3 Mon Sep 17 00:00:00 2001 From: Alexander Hess Date: Wed, 5 Dec 2018 13:34:09 +0100 Subject: [PATCH] Update readme file --- README.md | 27 +++++++++++++++++++-------- 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 8cbbce1..1eec963 100644 --- a/README.md +++ b/README.md @@ -2,22 +2,33 @@ ## General Notes -This project contains a Jupyter notebook introducing some very basic concepts of machine learning and the popular Iris classification case study. +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. +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. +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 a requirements.txt file. To do so, run `pip install -r requirements.txt`. It is recommended that a virtual environment be used. To do so, run `virtualenv --python=/usr/bin/python3 venv` on a Unix-like machine (Linux and iOS). +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/). -Alternatively, it can be started in the Anaconda environment (version 4.3.0, 64-bit) running Python 3.x. - -After installation, start Jupyter via running `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). +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. +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.