34 lines
1.4 KiB
Markdown
34 lines
1.4 KiB
Markdown
# Introduction to Machine Learning with Python
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## General Notes
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This project contains a Jupyter notebook introducing some very basic concepts
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of machine learning and the popular Iris classification case study.
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First, some simple linear algebra ideas are shown via examples with the numpy
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library. Then a so-called K-nearest-neighbor algorithm is trained to classify
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flower from the Iris dataset.
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This intro is aimed at total beginners to programming and machine learning.
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It was used within a 90 minute workshop at the
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[WHU Campus for Supply Chain Management](http://campus-for-scm.de), which
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targets students of business administration and young management professionals.
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## Installation
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This project uses popular Python libraries that can be installed via the
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pipenv command line tool. To do so, run `pipenv install` or
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`pipenv install --ignore-pipfile` (to use the exact environment as of the time
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of this writing). For a tutorial on pipenv, go to the official
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[documentation](https://pipenv.readthedocs.io/en/latest/).
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After installation, start Jupyter via the command `jupyter notebook` and wait
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for a new tab to be opened in your default web browser. Then, open the notebook
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called [intro_to_machine_learning.ipynb](intro_to_machine_learning.ipynb).
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## Read-only Version
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For those interested in just reading the example codes without installing
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anything, just open this [notebook](intro_to_machine_learning.ipynb) and view
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the Jupyter notebook in your browser.
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