1 Principal Component Analysis | PCA | Dimensionality Reduction in Machine Learning by Mahesh Huddar

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I noticed that your channel contains the entirety of Data Mining taught at the Master's level! Thank you very much, subscribing immediately!

[@MaheshHuddar]

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[@junaidahmad218]

This man has depth knowledge of this topic.

[@MaheshHuddar]

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[@ishu_official]

Super explanation.. today is my machine learning paper

[@MaheshHuddar]

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Mine is tomorrow!

[@nurulsyuhadah984]

How was it?

[@VDCreatures-kc6uf]

Super explanation..the best channel in KZbin to learn machine learning and ann topics ❤❤

[@MaheshHuddar]

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[@jambulingamlogababu8914]

Excellent Teaching. Salute to you sir

[@MaheshHuddar]

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[@NandeeshBilagi]

Clear and nice explanation. Thanks for the video

[@MaheshHuddar]

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[@venkateshwarlupurumala6283]

Very clear Explanation Sir.... Thank you so much...

[@MaheshHuddar]

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[@TrueTalenta]

Amazing step-by-step outline! I love it💌, so I subscribe!

[@MaheshHuddar]

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[@rodrigorcbb]

Thanks for the video. Great explanation!

[@MaheshHuddar]

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[@radhay4291]

Thank u very much.Very clear explanation and it is to understand

[@MaheshHuddar]

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[@varshabiradar1482]

You have explained very well. It will be good if you explain, "What is the use of calculating the pca in the geometrical representation you explained at the last?"

[@RaviShankar-gm9of]

Super Bhayya ...

[@thilagarajthangamuthu2935]

Thank you sir. Clear and easy to understand. Thank you.

[@MaheshHuddar]

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[@Straight_Forward615]

thanks a lot for this wonderful lecture.

[@MaheshHuddar]

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[@krishnachaitanya3089]

Thats a clear explanation i have seen

[@MaheshHuddar]

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[@PRANEETHAMALAKAPALLI]

tq sir for this wonderful concept

[@MaheshHuddar]

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[@User22_2g]

Great work sir! Really very helpful for my exams. Very grateful to you 🙏 One suggestion sir, kindly please share practice problems in the description or in comment box sir. Could you please do it as soon as possible sir? Thanks a lot sir 😀

[@priyalmaheta690]

content and teaching is very good please also provide the notes it will be helpful

[@MaheshHuddar]

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[@Dinesh-be8ys]

thank u for uploading like this video

[@MaheshHuddar]

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[@gamingaddaaa2141]

Thank you sir for the information

[@kapras711]

super explanation .. very easy to understand with out any hook ups sir thanks ...Inspr KVV.Prasad

[@MaheshHuddar]

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[@ashishlal7212]

Thank you so much today is my data mining and ML paper

[@MaheshHuddar]

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[@shubhangibaruah3940]

thank you sir, you were amazing🤩

[@MaheshHuddar]

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[@nikks9969]

Hello sir, thank you for your explanation.I have a doubt at 08:17 why you have considered only first equation?

[@MaheshHuddar]

You will get same answer with second equation You can use either first or second no issues

[@priya1912]

thank u so much

[@MaheshHuddar]

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[@yuva_india123]

Thanks sir for your explanation 🎉

[@MaheshHuddar]

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[@sinarezaei4288]

Thank you very much master huddar❤

[@MaheshHuddar]

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[@vineshspidy4825]

Thankyou

[@SaifMohamed-de8uo]

thank you so much you are great professor

[@MaheshHuddar]

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[@AssignmentsIT]

Thank you so much sir amazing explaination♥♥♥

[@MaheshHuddar]

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[@StoriesFromWeb13]

thank u sir wonderful

[@srinivas664]

Nice presentation tq sir

[@yashtiwari4696]

Sir please upload the content of ensemble methods bagging boosting and random forest

[@MaheshHuddar]

Ensemble Learning: kzbin.info/www/bejne/m3_chpmmfKd_qtU Random Forest: kzbin.info/www/bejne/oYHUZGVuoq-Be5I

[@Husain8570]

Best explanation

[@Blackoutfor10days]

Can you add the concept of hidden Markov model in your machine learning playlist

[@MaheshHuddar]

Sure Working on it

[@Blackoutfor10days]

@@MaheshHuddar okay 👍

[@Blackoutfor10days]

@@MaheshHuddar my exam is near

[@waidapapa1514]

Why we are not dealing with e2 means why we not do e2^T.[cov matrix]

[@rohanshah8129]

Here, we had considered 2 dimension as the high dimensonal data for example. One of the most usecase of PCA is in dimensionality reduction. So, if you want you can use e2 and get second PC. But then think about it. From 2 variable, we again got 2 variables. That's why he has shown only PC1. However, in reality we generally use 2 PC axes (mostly depends on your data). If it has a lot of variables, then 3 or 4 can also be good but we don't generally go beyond that. So, in this case you will need e2, e3 and e4 as well. So this is how it works.

[@aravind25]

Should we consider e2 in place of e1​@@rohanshah8129

[@putridisperindag6986]

thank you very much Sir, for ur explantion on that video. I still confused so I would like to ask how to get the value of: [-4.3052, 3.7361, 5.6928, -5.1238] how can I get the value. I still dont get. Thank u Sir

[@jvbrothers5454]

yeahh im also confused how did he get im getting values diffrent 0.3761 5.6928 -5.128

[@zafar151]

Excellent

[@MaheshHuddar]

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[@Ateeq10]

Thank you

[@MaheshHuddar]

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Thank you very much sir

[@MaheshHuddar]

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[@mango-strawberry]

thanks a lot

[@MaheshHuddar]

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[@muhammadsaad3793]

Nice!

[@MaheshHuddar]

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[@brucewayne.64]

Thanks Sir

[@MaheshHuddar]

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[@RaviShankar-gm9of]

linear discriminent analysis please make a video bhayya

[@Abhilashaisgood]

thankyou sirr, how to calculate 2nd pc?

[@MaheshHuddar]

Select the second eigen vector and multiply to the given feature matrix

[@affanahmedkhan7362]

❤❤

[@advancedappliedandpuremath]

Sir book name please

[@demodemo-o4z]

Hi Sir, Great explanation about PCA. But when I searched the covariance matrix for more 2 variables it's showing that covariance is only done between 2 variables. How to calculate the covariance if a dataset have more than 2 variables. Could you please give an explanation on that.....!!

[@fintech1378]

you need to do for all pairwise combinations

[@shahmirkhan1502]

@fintech1378 is right. You need to do pairwise combinations. For example, for 4 variables, your covariance matrix will be 4x4 with the following combinations: cov(a, a) cov (a, b) cov (a, c) cov(a,d) cov(b, a) cov(b, b) cov(b, c) cov(b, d) cov(c, a) cov (c, b) cov(c, c) cov(c, d) cov(d, a) cov(d, b) cov (d, c) cov(d, d)

[@rohanshah8129]

If there are n variables, covariance matrix will be of nxn shape.

[@parthibdey6005]

is this covariance for reducing 4 to 1@@shahmirkhan1502

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[@MaheshHuddar]

What it means..?

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@@MaheshHuddar According to google translate: _"I love you sooo"_

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devru sir neevu

[@MaheshHuddar]

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Thanks you,sir

[@MaheshHuddar]

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