Posts

This story was originally written for “Augmenting Writing Skills for Articulating Research (AWSAR)” award 2018. It is written in a non-technical way so as to be accessible to as many people as possible irrespective of their educational background.

Almost every reader would have seen systems of linear equations from their high school days. Whether they liked it or not is a separate story. But, in all likelihood, they would have solved these equations by gradually removing variables one by one by substitution.

Run Python code in Google Colab Download Python code Download R code (R Markdown) In this post, we will reproduce the results of a popular paper on PCA. The paper is titled ‘Principal component analysis’ and is authored by Herve Abdi and Lynne J.

Run Python code in Google Colab Download Python code Download R code (R Markdown) Download MATLAB code This post is Part-II of a three part series post on PCA. Other parts of the series can be found at the links below.

In this post, we will discuss about Principal Component Analysis (PCA), one of the most popular dimensionality reduction techniques used in machine learning. Applications of PCA and its variants are ubiquitous.