# covariance

## Linear Classifiers: An Overview

Linear Classifiers: An OverviewThis article discusses the mathematical properties and practical Python applications of four popular linear classification methods. Michał OleszakBlockedUnblockFollowFollowingMay…

## An overview of Principal Component Analysis

How will it look?Variance is the expectation of the squared deviation of a random variable from its mean. Informally, it…

## Principal Component Analysis — Math and Intuition (Post 3)

I hope you chose the black line as it is closer to most of the data points (see figure below).…

## PCA and SVD explained with numpy

PCA and SVD explained with numpyZichen WangBlockedUnblockFollowFollowingMar 16How exactly are principal component analysis and singular value decomposition related and how to…

## Optimal Portfolios: Why and How?

Optimal Portfolios: Why and How?Vivek PalaniappanBlockedUnblockFollowFollowingFeb 3Portfolio optimization is a crucial part of managing risk and maximizing returns from a set…

## A Gentle Introduction to Deep Learning : Part 3

A Gentle Introduction to Deep Learning : Part 3PCA & Linear Algebra(Advance)Akshat JainBlockedUnblockFollowFollowingJan 27Photo by Antoine Dautry“You can’t build great building on a…

## Baffled by Covariance and Correlation??? Get the Math and the Application in Analytics for both the terms..

The values from PCA done using the correlation matrix are closer to each other and more uniform as compared to…

## The Mathematics Behind Principal Component Analysis

The whole process of obtaining principle components from a raw dataset can be simplified in six parts :Take the whole dataset…