Principal Component Analysis (PCA) with Example | Machine Learning Tutorial

[@bhavikdudhrejiya4478]

While watching this video the below equation is not solved hence I have solved it for further clarity. Making Quadratic Equation:- (1.67-λ)(2.73-λ)-(2.083)^2 = 0 (4.5591)-(1.67λ)-(2.73λ)+λ^2-4.33=0 λ^2-4.4λ+0.2291=0 Solving Quadratic Equation [Formula: x=(-b+-√b^2-4ac)/2a]:- -4.4 + SQRT((4.4)^2-4(1)(0.2291))/2(1)----Adding -4.4 - SQRT((4.4)^2-4(1)(0.2291))/2(1)-----Subtracting λ1 = 4.3473 & λ2 = 0.0527

[@nikhilpawar9910]

Can you explain how got value of a11

[@shwetkumar8370]

If you are attempting to keep the video length short, just explain the key point and display the calculating processes by performing that first. It doesn't matter how long the video is as long as the main goal is to help people understand the concept.

[@nitinprajapati869]

Keep it up bro 👍

[@AdhayanAcademy]

How to calculated orthogonal matrix explained

[@mateensk93]

Orthogonal transformation ke value kaisa aaye .. plz pura pura solve kiya karo

[@turtlepedia5149]

Very nice video

[@Sandeepkumar-kd2ow]

which value for x1 and x2

[@continuebhai4762]

Thanku so Much sir

[@yaminiwarke3569]

covariance of X,Y is zero and u r saying it is 2.003 how???please explain

[@aaronpaulhughes]

the covariance is defined as cov(A,B) = 1/(N−1) * sum (Ai−μA)∗(Bi−μB) So you're taking all each element of A - meanA times B - meanB and summing them like so below. (2-0.5)*(4-2.125)=2.813 (1-0.5)*(3-2.125)=0.4375 (0-0.5)*(1-2.125)=0.5625 (-1-0.5)*(0.5-2.125)=2.438 All of the above sums to 6.25 and then divide by N-1=3 So 6.25/3 = 2.083

[@priyrajthequirkycouple7183]

thankyou sir

[@AbhishekSharma-zb9wr]

Sir speed thodi jayda h........

[@shatadruroychowdhury6319]

It's called Identity matrix, not identical matrix.

[@syedmsohaib5745]

To many ads in mid of video

[@nehabansal8471]

making too hurry while speaking... getting irritate.. content is good but explanation is very bad just bcoz of hurry to complete the video..

[@dineshpramanik2571]

Thoda relax ho k padhao bhai...