alexander/intro-to-data-science
1
1
Fork 0
Code Issues Pull requests Releases Activity

Update readme file

Browse source
This commit is contained in:
Alexander Hess 2018-12-05 13:34:09 +01:00
parent 4ce227851f
commit b3e1a761e8
1 changed files with 19 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

27
README.md
View file

@ -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.