Unsupervised Learning algorithms cheat sheet

This repository provides cheat sheets for different unsupervised learning machine learning concepts and algorithms. This is not a complete tutorial, but it can help you better understand the structure of machine learning or to refresh your memory.

The content is presented in different formats:

• read the original article on Towards Data Science - click here;
• read on GitLab directly in the markdown format: full cheat sheet - click here or by chapters - click here;
• read the PDF version offline or print - click here

Download the PDF version

Download the PDF version of a unsupervised learning algorithms cheat sheet: click here.

Contents

The following tasks and algorithms are mentioned:

• Dimensionality Reduction:
  • Principal Component Analysis
  • Manifold Learning - LLE, Isomap, t-SNE
  • Autoencoders and others
• Anomaly Detection:
  • Isolation Forest
  • Local Outlier Factor
  • Minimum Covariance Determinant and other algorithms from dimensionality reduction or supervised learning
• Clustering:
  • K-Means;
  • Hierarchical Clustering and Spectral Clustering;
  • DBSCAN and OPTICS;
  • Affinity Propagation;
  • Mean Shift and BIRCH;
  • Gaussian Mixture Models.
• Density Estimation;
• Association Rule Learning.