pca: A Python Package for Principal Component Analysis.

We perform PCA for both visualization and feature selection here.

Using Non-negative Matrix Factorization (NMF) and Variational Autoencoder (VAE) machine learning architectures to analyze spatial and spectral features of hyperspectral cathodoluminescence (CL) spectroscopy images taken from hybrid inorganic-organic perovskite material

Faces recognition example using eigenfaces and SVMs

NU Bootcamp Module 19

Health Profile Analysis:Revealing Disorder Paterns,Medication Guidance and Risk Classification-ML Project

This project highlights the importance of dimensionality reduction by exploring 2 Machine learning techniques called "Principal Component Analysis" and "T-SNE".

Scripts from ML course 02459 from Technical University of Denmark. Scripts have been modified for custom use (e.g. automation of various things, use of pandas rather than numpy arrays and such).