This is hands down the best PCA explanation I have seen on the internet. Period!

गुरुर्ब्रह्मा ग्रुरुर्विष्णुः गुरुर्देवो महेश्वरः। गुरुः साक्षात् परं ब्रह्म तस्मै श्री गुरवे नमः।। you are the real teacher sir.. 💫

I have studied eigenvalues and eigenvectors, multiple times, but this video explained to me the depth of it in a very simple way! One of the best teachers out there.

Feature extraction perfect example is u sir High dimension knowledge ko 2D me convert krk smjhne layak bana dete ho.. Thankq so much sir.

I am always amazed how important the concept of eigenvectors and eigenvalues are, they are one of the most important concepts of quantum mechanics. Every operator( ex- energy, momentum) in Q.Mech is a linear operator and our aim usually is to find the corresponding eigenvectors and eigenvalues. Time-independent Schrödinger eq usually takes the form of eigenvalue equation Hψ =Eψ. It's so amazing to see how these concepts are finding their role in Machine Learning as well. MY love for Math keeps on growing. As always thank you for your amazing videos

You are simply amazing. I bet no one can teach PCA so well.

No one has ever explained Eigen Vectors in such a simple way, You are awesome !!

Honestly everything I know, I owe it to you, thankyou for being the real HERO in the need!!!

those who are wondering why three eigen vectors everytime, because covariance matrix is a symmetric matrix, and Real Symmetric Matrices have n linearly independent and orthogonal eigenvectors. zero vector is not considered an eigen vector although it satisfies Ax= λx, like wise there might be upto n LI eigen vectors for n*n symmetrix matrix

Best explanation of PCA I've ever seen!! ❤️

Omg in one video he explains the most difficult linear algebra topic and applied it to machine learning and also showed us the code. Hats off

This is the best video I have watched on this topic!

This Channel is Goldmine 🙌

Amazingly explained. 🤩👏🏻

This is called Teaching ! Thanks for this wonderful explanation.

u r the best Nitish. Thanks for all these.

what an amzing explanation very intutive am follwing ur whole series sir

you have made a complex topic like PCA so easy for us to understand

your content is best ever!! thank your sir!

One of the best videos I found for PCA. You have great skills brother.

The most satisfying Machine Learning lecture that I've ever seen by far🤩🤩

You have cleared my concept which was not well explained by any other instructor on youtube! Great job❤❤

this channel is so underated,by seeing this pca video everyone one can understand dimensonality reduction,thank you sir for the hard work

My head hurts, this is sooo descriptive and apt and worthy enough of all the time. best. Kuddos

extremely superb explanation, kudooss

Speechless ,too good to grasp

Thank you so much sir, you always leave us awestruck by your remarkable explanation and in-depth knowledge. I never knew this topic can be explained with this much clearity. The teacher I never knew I needed in my life ❤️✨

The Epic PCA explaination ever seen on KZfaq and never done by any DS KZfaqr Yet, Hat's off to your teaching skills sir

I am blown away by understanding the true meaning of eigen vectors. I always knew the definition but now I have understood the meaning. You are a savior!

Wow best video on PCA on internet

Awesome video, omg , u explained every concept so clearly. thx a lot sir

how can i thank you, what a wonderful teacher available for free for the help of many students

Sir No one in KZfaq taught this much intutive. Even paid course cant teach this much indepth

Thank You sir. such a crisp explaination....

45:44 I think it's gonna be (3,1) and when transposed it's gonna be (1,3) which then is multiplied with the matrix representing the dataset. (1,3) × (3, 1000) . This representation is valid too

Thanks sir for this amazing explanation

Brilliant explanation! Thank you so much :)

Great step-by-step explanation

Best Teaching Skills I have ever seen ,for All Machine Learning concepts, Hats of you Sir!🎉🎉🎊🎊

I have seen and understand lin alg playlist at 3blue1 brown But you enhances my doubts clarifications even more thanks

Amazing clarity !!!

Maza aagaya bhai itna achha explanation sayad hi kisine youtube me kiya hoga.

Amazing Tutorial

sir ye dekh kar maja ko maja aaa gaya. kya hi bolu sir ab . PCA ka Best video tha ye. Love you sir.

god bless you. wonderful session

you are a saviour to my sinking boat❣. thanks a lot.

Thankyou So Much Sir, You not only Cleared my doubt's about how PCA works, but also for the first time gave me mathematical intitution of Eigen Value and Eigen Vector and even Matrices Transformation which I am learning from previous so many years Best Explaination I've seen regarding this topic

गुरू ब्रह्मा गुरू विष्णु, गुरु देवो महेश्वरा गुरु साक्षात परब्रह्म, तस्मै श्री गुरुवे नमः Aapko koti koti naman hai sir, jo ye gyan aapne youtube ke jariye hum sabko diya

Love from KARACHI ,PAKISTAN.

Actually I was learning PCA for the first time. When I watched the video for the first time I didn't understand it but when I watched it a second time then all the topics very clearly. This video is amazing

very excellent explanation.....

was very useful for me, thank you :)

Thanks for explanations!

Great explanation @Nitish

Thankyou sir for this amazing video.

You are best in business

Justt soo awesome ! Cant describe !

Excellent!!

excellent explanation

Thank You Sir.

Awesome 👏👍🏻

Pure Gold

One small explanation of shortcut in lecture at 16:04, co-variance actual formula includes xmean and ymean, here both were zero, that's why shortcut sum(x*y)/3 formula for covariance is: covariance(x,y) = summation[(x-xmean) (y-ymean)] / n basically this is the same reason, covariance matrix has variance at diagonal 22:57 both features are same x so covariance(x,x) = summation[(x-xmean)(x-xmean)]/n which is actually the formula for variance

The best explanation

00:02 PCA aims to reduce dimensionality while maintaining data essence. 02:55 Projection and unit vector for PCA 10:27 Principle Component Analysis (PCA) helps to find the direction for maximum variance. 12:48 Variance measures the spread of data 19:22 Principal Component Analysis (PCA) helps in understanding the spread and orientation of data. 21:56 PCA provides complete information about data spread and orientation. 27:10 Principle Component Analysis involves transformations and changing directions of vectors. 29:39 Linear transformation does not change vector direction. 34:24 Principal Component Analysis (PCA) uses eigenvectors for linear transformation. 36:36 Principal Component Analysis (PCA) helps identify vectors with the highest variation in data. 41:55 Principal Component Analysis allows transforming data and creating new dimensions. 44:15 PCA involves transforming the dataset to a new coordinate system 49:14 Using PCA to find the best two-dimensional representation of 3D data 52:07 Principle component analysis (PCA) involves transforming and transporting the data. Crafted by Merlin AI.

pretty good explanation of doing PCA computatinally without using the sklearn

clearly explained

wow is my 1 st expression after watching this vedio....

Excellent !!!

Thanks for the excellent video bro ,@16:21 in covariance we substrate values from mean and then multiply right

😭😭😭😭thanks a lot sir, thank you so much

I am Gonna Mad😶 ; You are Truly Legend 🔥

Speechless .....you deserve a million subscribers at least

very very valuable.

Thank you sir

Amazing!

wow kudus fot the explanation

This is even better than Josh Starmer's video.

wow content ! you are playing a big role for me to make me as a data scientist .. thank you sir!

You are ML guru, 🙏

Request you to continue deep learning series

love you sir ❣ crush updated

guru G aapke charan kha h ...GOD bless you sir

Best!

bhai chad explanation hai

You are really a good teacher, I am in IIT Bombay, Environmental Engineering, Mtech , but I wanted to learn ML, this playlist is so far best understandable for me.

Do a series on time series analysis and NLP please

Sir, I want to know ki how libraries are working , agar aap uspe basic explanation batade , baki to this series is very awesome .

One stop everything

best💗

One small correction: 52:55 Eigen Vectors are the COLUMNS of the matrix which is given as the output of np.linalg.eig() not the rows which you have used... please correct me if I am wrong

wow sir thanks, you are the best pr apne aage videos Q nhi bnayi hai unsupervised learning p mein wt kr rha hu plz reply

best

one question do we need to sort the eigen vectors based on highest eigen values and then choose the eigen vectors accordingly? also sum of top K eigen values will show us how many eigen vectors we need to take (in case of high dimensional data)

most elegant explanation of Eigen Vector and Eigen values ever seen. Thanks sir for this one

@CampusX, Sir please help with t-Sne Algorithm also

Hi Sir just to understand the concept well, so when we do the transformation of the data D, do we use the matrix of Eigen Vector (Calculated using the Covariance Matrix) or do we use the Covariance Matrix itself? Its using the matrix of Eigen Vector right?

Is it possible to have an example of pictures to classify them into two categories? If the dimensions are reduced in pca and classification in knn is better , please

I think, There can only be n eigen values for a n*n matrix. And n unit eigen vectors for it. but can be as many as eigen vectors as possible. we need to just multiply by some k to to that unit eigen vectors to get some more eigen vectors. :)

sabse bada eigenvector sabse bade eigen value ke corresponding hoga , but ek eigen value ke corresponding more than one eigen vector hotai hai , infact poora eigen space hota hai (except 0 vector ofcourse)!! in R2 plane ,it will have uncountable eigen vectors corresponding to the largest eigen value