Machine-Learning-OER-Basics

This repository contains teaching material for machine learning which is labelled under a free license.

You are welcome to reuse them for courses, presentations, workshops, etc.
Please, give appropriate credit to the authors.

The repository is structured as follows:

For each algorithm, teaching material is provided as code, images and text snippets.

Content

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Supervised learning

• Classification
  • k-nearest neighbors
  • Exploratory Data Analysis (EDA) for
  • decision tree classifier
  • ensemble methods
    • random forest classifier
    • gradient boosting classifier
• Regression
  • linear models

In planning:

• naive bayes

Unsupervised learning

• support vector machines
• neural network models

Reinforcement learning

The repo follows the book Introduction to Machine Learning with Python by Andreas Mueller and Sarah Guido. For more information, see the repo introduction_to_ml_with_python.

Requirements

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The code is written in Python 3.9 and uses the requirements listed in the file requirements.txt. You can install the requirements with the following commands:

git clone https://github.com/Machine-Learning-OER-Collection/Machine-Learning-OER-Basics

cd Machine-Learning-OER-Basics

pip install -r requirements.txt

Or you can try out Binder by clicking on the following badge:

Contributing

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Please read the Contributing Guidelines if you want to contribute.

Feel free to open a GitHub issue if you have any questions.

License

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Please read the License for more information.

You can find further information about the Creative Commons licenses under:

• Creative Commons
• CC Wiki Best Practices for attribution
• CC Wiki

Teaching material is available under a CC-BY 4.0 and MIT license. The kick data set is available under a CC0 license.

Contributors

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• ischmahl contributed knn and linear regression
• julia contributed EDA, decision tree, random forest and gradient boosting classifier