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.

• Linear Regression
• Logistic Regression
• Perceptron
• k-nearest-neighbor
• k-Means clustering
• Simple neural network with one hidden layer
• Multinomial Logistic Regression
• Decision tree for classification
• Decision tree for regression

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.

• Image preprocessing
• Preprocessing a numerical/categorical dataset

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.

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