A one-stop for unsupervised learning algorithms implemented from scratch in python.
This repository is meant to be a place to study algorithms thoroughly and learn how to implement the theoretical concept. While there are libraries available like scikit-learn which can be used to import implementable models, using them can lead to a gap in understanding of the algorithm itself.
Take the time, learn your algorithm and code!
This repository is specfically for unsupervised learning algorithms only. These algorithms are usually used to solve some specific types of problems:
- Clustering: Finding structures or patterns in a collection of uncategorized data.
- Association: Discovering rules which describe relationships in your data.
- Component Analysis: This is a broad category in which I have clubbed algorithms for dimensionality reduction like PCA.
Some algorithms which can be added here are:
- Mean shift clustering
- Heirarchical clustering
- BIRCH clustering
- IsoMap
Check out this doc for details about how to contribute! Contributions are always welcome, and feel free to contact me for any help.