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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.

• Bayesian Linear Regression

• Decision tree for classification

• Decision tree for regression

• k-nearest-neighbor

• k-Means clustering

• Linear Regression

• Logistic Regression

• Multinomial Logistic Regression

• Perceptron

• Principal Component Analysis

• Simple neural network with one hidden layer

• Softmax regression

• Support vector machines

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.

• Image preprocessing
• Preprocessing a numerical/categorical dataset

Live demo

Run the notebooks online without having to clone the repository or install jupyter: .

Note: this does not work for the data_preprocessing.ipynb and image_preprocessing.ipynb notebooks because they require downloading a dataset first.

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).