https://github.com/zotroneneis/machine_learning_basics

Plain python implementations of basic machine learning algorithms
https://github.com/zotroneneis/machine_learning_basics

algorithm ipynb k-nearest-neighbor k-nearest-neighbours k-nn kmeans linear-regression logistic-regression machine-learning machine-learning-algorithms neural-network neural-networks perceptron python python-implementations python3

Last synced: 4 days ago
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Plain python implementations of basic machine learning algorithms

• Host: GitHub
• URL: https://github.com/zotroneneis/machine_learning_basics
• Owner: zotroneneis
• License: mit
• Created: 2018-02-19T09:55:58.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-06-27T11:21:33.000Z (6 months ago)
• Last Synced: 2024-12-03T09:07:29.118Z (11 days ago)
• Topics: algorithm, ipynb, k-nearest-neighbor, k-nearest-neighbours, k-nn, kmeans, linear-regression, logistic-regression, machine-learning, machine-learning-algorithms, neural-network, neural-networks, perceptron, python, python-implementations, python3
• Language: Jupyter Notebook
• Size: 10.8 MB
• Stars: 4,321
• Watchers: 164
• Forks: 835
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE
  • Support: support_vector_machines.ipynb

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README

        
# 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](bayesian_linear_regression.ipynb)

- [Decision tree for classification](decision_tree_classification.ipynb)

- [Decision tree for regression](decision_tree_regression.ipynb)

- [k-nearest-neighbor](k_nearest_neighbour.ipynb)

- [k-Means clustering](kmeans.ipynb)

- [Linear Regression](linear_regression.ipynb)

- [Logistic Regression](logistic_regression.ipynb)

- [Multinomial Logistic Regression](softmax_regression.ipynb)

- [Perceptron](perceptron.ipynb)

- [Principal Component Analysis]([principal_component_analysis.ipynb)

- [Simple neural network with one hidden layer](simple_neural_net.ipynb)

- [Softmax regression](softmax_regression.ipynb)

- [Support vector machines](support_vector_machines.ipynb)

  

  

![alt text](figures/decision_tree_predictions.png)

## 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](image_preprocessing.ipynb)

- [Preprocessing a numerical/categorical dataset](data_preprocessing.ipynb)

![alt text](figures/image_preprocessing.png)

## Live demo

Run the notebooks online without having to clone the repository or install jupyter: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/zotroneneis/machine_learning_basics/HEAD).   

   

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).