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

Principal Component Analysis | PCA | Dimensionality Reduction in Machine Learning by Mahesh Huddar
The following concepts are discussed:
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[@kenway346]

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

[@MaheshHuddar]

Welcome
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[@ishu_official]

Super explanation.. today is my machine learning paper

[@MaheshHuddar]

Thanks and welcome
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[@ankitjha_03]

Mine is tomorrow!

[@RaviShankar-gm9of]

Super Bhayya ...

[@VDCreatures-kc6uf]

Super explanation..the best channel in UA-cam to learn machine learning and ann topics ❤❤

[@MaheshHuddar]

Thank You
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[@TrueTalenta]

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

[@MaheshHuddar]

Thank You
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[@rodrigorcbb]

Thanks for the video. Great explanation!

[@MaheshHuddar]

Welcome
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[@jambulingamlogababu8914]

Excellent Teaching. Salute to you sir

[@MaheshHuddar]

Welcome
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[@kapras711]

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

[@MaheshHuddar]

Thank You
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[@thilagarajthangamuthu2935]

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

[@MaheshHuddar]

Welcome
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[@venkateshwarlupurumala6283]

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

[@MaheshHuddar]

Welcome
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[@junaidahmad218]

This man has depth knowledge of this topic.

[@MaheshHuddar]

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[@radhay4291]

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

[@MaheshHuddar]

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[@yuvagopi6547]

Thanks sir for your explanation 🎉

[@MaheshHuddar]

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[@NandeeshBilagi]

Clear and nice explanation. Thanks for the video

[@MaheshHuddar]

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[@Dinesh-be8ys]

thank u for uploading like this video

[@MaheshHuddar]

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[@HamidAli-ff2zn]

Thank you so much sir amazing explaination♥♥♥

[@MaheshHuddar]

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[@krishnachaitanya3089]

Thats a clear explanation i have seen

[@MaheshHuddar]

Thank you
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[@Straight_Forward615]

thanks a lot for this wonderful lecture.

[@MaheshHuddar]

Welcome!
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[@user-qz4yu5lo1r]

tq sir for this wonderful concept

[@MaheshHuddar]

Welcome
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[@SaifMohamed-de8uo]

thank you so much you are great professor

[@MaheshHuddar]

You are very welcome
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[@shubhangibaruah3940]

thank you sir, you were amazing🤩

[@MaheshHuddar]

Welcome
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[@sinarezaei4288]

Thank you very much master huddar❤

[@MaheshHuddar]

Welcome
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[@ashishlal7212]

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

[@MaheshHuddar]

Welcome
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[@yashtiwari4696]

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

[@MaheshHuddar]

Ensemble Learning: ua-cam.com/video/eNyUfpGBLts/v-deo.html
Random Forest: ua-cam.com/video/kPq328mJNE0/v-deo.html

[@priyalmaheta690]

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

[@MaheshHuddar]

Thank You
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[@RaviShankar-gm9of]

linear discriminent analysis please make a video bhayya

[@user-of6qi2hk1e]

Thank you very much sir

[@MaheshHuddar]

Welcome
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[@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.

[@priya1912]

thank u so much

[@MaheshHuddar]

Welcome
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[@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]

Thank You
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[@brucewayne.64]

Thanks Sir

[@MaheshHuddar]

Welcome
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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

[@mango-strawberry]

thanks a lot

[@MaheshHuddar]

You are most welcome
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[@MadaraUchiha-wj8sl]

Thanks you,sir

[@MaheshHuddar]

Welcome
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[@advancedappliedandpuremath]

Sir book name please

[@muhammadsaad3793]

Nice!

[@MaheshHuddar]

Thank You
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[@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

[@Ateeq10]

Thank you

[@MaheshHuddar]

Welcome
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[@user-kn7sr8tg4q]

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

[@user-of4rz5cq1i]

بحبككككككككككككككككككككككككككككككككككككككككك يا سوسو

[@MaheshHuddar]

What it means..?

[@abishekraju4521]

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

[@jameykeller5055]

devru sir neevu

[@MaheshHuddar]

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