A collection of Jupyter Notebook guides on various Machine Learning methods, including theoretical explanations and practical implementations. Compatible with Google Colab for easy execution.

Principal component analysis (PCA) is one of the most common exploratory data analysis techniques with applications in outlier detection, dimensionality reduction, graphical clustering, and ...

This project contains code for the examples in the the technical report. Kernel Principal Components Analysis is a non-linear extension of Principal Components Analysis (PCA) using kernel functions.