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Plain python implementations of basic machine learning algorithms
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figures figure added Jun 17, 2018
LICENSE Create LICENSE Mar 12, 2018
README.md Update README.md Jul 14, 2018
data_preprocessing.ipynb minor changes in display of dataframe Jul 14, 2018
decision_tree_classification.ipynb figures folder created, paths adapted Apr 17, 2018
decision_tree_regression.ipynb figures folder created, paths adapted Apr 17, 2018
image_preprocessing.ipynb
k_nearest_neighbour.ipynb typos fixed, plt.show() added Mar 26, 2018
kmeans.ipynb
linear_regression.ipynb typo fixed, figure title added Mar 26, 2018
logistic_regression.ipynb
perceptron.ipynb
simple_neural_net.ipynb figures folder created, paths adapted Apr 17, 2018
softmax_regression.ipynb

README.md

Machine learning basics

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations.

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Data preprocessing

After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

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Feedback

If you have a favorite algorithm that should be included or spot a mistake in one of the notebooks, please let me know by creating a new issue.

License

See the LICENSE file for license rights and limitations (MIT).

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