23 lines
1.5 KiB
Markdown
23 lines
1.5 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 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 library. Then a so-called K-nearest-neighbor algorithm is trained to classify 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 [WHU Campus for Supply Chain Management](http://campus-for-scm.de), which 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 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).
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Alternatively, it can be started in the Anaconda environment (version 4.3.0, 64-bit) running Python 3.x.
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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).
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## Read-only Version
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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.
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