Using Python Generators

Run in Google Colab View source on GitHub Download notebook In this post, we will discuss about generators in python. In this age of big data it is not unlikely to encounter a large dataset that can’t be loaded into RAM. In such scenarios, it is natural to extract workable chunks of data and work on it. Generators help us do just that.

Principal Component Analysis - Part III

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. Williams. It got published in 2010 and since then its popularity has only grown. Its number of citations are more than 4800 as per Google Scholar data (This was the number when this post was last revised).

Principal Component Analysis - Part II

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. Part-I: Basic Theory of PCA Part-III: Reproducing results of a published paper on PCA In this post, we will first apply built in commands to obtain results and then show how the same results can be obtained without using built-in commands.

Principal Component Analysis - Part I

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. Thus, a through understanding of PCA is considered essential to start one’s journey into machine learning. In this and subsequent posts, we will first briefly discuss relevant theory of PCA. Then we will implement PCA from scratch without using any built-in function.