Basics Of Principal Component Analysis Part-1 Explained in Hindi ll Machine Learning Course

[@artyCrafty4564]

4 imp points from video -- 1. pca solves the problem of overfitting 2. pca reduces high dimensionality dataset to low dimensionality 3. the number of pcs can be less than or equal to the number of attributes. although pc also depend on other factor such as dimensionality. 4. pcs should be orthogonal that is should be independent from each other

[@Saif1412-y1v]

Thanks bro

[@SujitKUmar-gy5xr]

Thanks

[@prathmeshphatake1948]

01:09 PCA helps in overcoming the problem of overfitting caused by too many attributes and features during the training phase. 02:18 Principal Component Analysis (PCA) helps reduce overfitting 03:27 Principal component analysis helps in reducing overfitting by reducing dimensions and finding principal components 04:36 Principal components can be found using views to analyze the data from different perspectives. 05:45 The model generated two principal components: PC1 and PC2. 06:54 Principal components can be generated from multiple attributes and reduce the dimensionality 08:03 Give highest importance to PC1 and reduce priority for other principal components. 09:07 Principal Component Analysis (PCA) explained in a nutshell

[@sereto7867]

Thank you for saving our career ❤️

[@lunapotter5593]

PCA: Need: overfitting, many attributes and features before training need to reduce Pca reduce overfitting, model is trying to reach every point in overfitting, High Dimensionality to low dimensionality. Views: from top PC1, from another point PC2, PC1 Higher priority, Pc1 and pc2 must have orthogonal property i.e. independent of each other.

[@devangraut6916]

01:09 PCA helps in overcoming the problem of overfitting caused by too many attributes and features during the training phase. 02:18 Principal Component Analysis (PCA) helps reduce overfitting 03:27 Principal component analysis helps in reducing overfitting by reducing dimensions and finding principal components 04:36 Principal components can be found using views to analyze the data from different perspectives. 05:45 The model generated two principal components: PC1 and PC2. 06:54 Principal components can be generated from multiple attributes and reduce the dimensionality 08:03 Give highest importance to PC1 and reduce priority for other principal components. 09:07 Principal Component Analysis (PCA) explained in a nutshell Crafted by Merlin AI.

[@basudhasakshyarika1592]

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Because we start searching for videos only one day before the exam😂

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But why do you guys wait for the last moment??

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It's a talent possessed only by back benchers 😂🤣🤣

[@kaustubh7304]

Huge respects sir !!! You are surely 100% better than those University lecturers !! Because of u I can easily clear my concepts of ML, ERTOS, ICS ! Thank you so much for the help !!! I really appreciate that you are doing this with no returns and just giving away free education !! Hats off !!!!

[@johnwicckk]

He is best man!! Amazing learning videos. During every exam paper he is there to help. Thanka sir more power to you!

[@DoomedVortex]

I never knew Rohit Sharma was this good at ML. Way to go champ

[@subrotodebnath7680]

01:09 PCA helps in overcoming the problem of overfitting caused by too many attributes and features during the training phase. 02:18 Principal Component Analysis (PCA) helps reduce overfitting 03:27 Principal component analysis helps in reducing overfitting by reducing dimensions and finding principal components 04:36 Principal components can be found using views to analyze the data from different perspectives. 05:45 The model generated two principal components: PC1 and PC2. 06:54 Principal components can be generated from multiple attributes and reduce the dimensionality 08:03 Give highest importance to PC1 and reduce priority for other principal components. 09:07 Principal Component Analysis (PCA) explained in a nutshell Crafted by SUBROTO

[@manujpande8544]

Brother thanks yaar itna simple tareke se padha dete ho ki maza aa jata hai...please bhaiyon like karo isse aur subscribe bhi ....thanks yaar.

[@prasanthkumar6393]

100% satisfaction is guaranteed on a topic while watching your videos sir. Thank you so much

[@faizejafri1014]

Best Tutorial Found on KZbin...!!

[@alimehmood8654]

Question: When we cast our attributes on PC1, all the attributes get casted on the line, same goes for PC2. all the points get casted on PC2. Then how are they independent? We can find the same point on PC1 as well as PC2 (my assumption).

[@pranay6708]

It was so much confusing topic and you made it so much easy.., thanks a ton sir..

[@Lastmomenttuitions]

good explanation buddy

[@pradumnasoni1652]

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[@Bhatonia_Jaat]

at least he is supporting the better content without being arrogant! peet pe laat wali baat ni h.

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Wha LMT ki comment ❤️

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Topic search karte waqt kuch topics se releted apke videos milte nhi hai lekin ek video bi agar mil jaye toh bas baat khatam chere pe ek alag khushi ho jati hai 😁😁

[@sam9620]

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Tu US ke university proffessors se padh raha hai toh youtube pe kya kar raha hai bhai?

[@sahil2pradhan]

This is video is far better than my college professors lecture.

[@vaibhavdiwan1569]

According to Andrew ng machine learning course use of pca should be done for increasing the speed of learning algorithm instead of preventing over fitting use regularisation to prevent overfitting

[@muhammadiqbalbazmi9275]

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Kitne marks paye?

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yas!

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[@sshubam]

THANKYOU SIR i just love the energy with which you teach. thankyousomuch sir you are a great teacher.

[@a-archanabichkule]

Happy teacher's day 💐💐

[@Baetu123]

Thanks!

[@ASh-hb1ub]

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[@samiuddin6696]

You explained a very complicated idea in very easy tips. Thanks brother. Shaanti rahain.

[@Hayat26474]

Huge respects sir !! Thank you sir

[@adityakumarmishra8734]

I came just to understand PCA but I loved your way of explaning and now I am a new subsciber.

[@saqib317]

Appreciate your effort. Your videos are very informative and easy to understand.

[@ompandya30]

salute sir !!! you explained very nice compare to university teacher i can clear my concept of ml thankyou sir hudge respects for you sir keep it up!! sir

[@sonalisingh2136]

Well i must appreciate......your work.......I just wanna thank you of reducing time and coming to the point .......... I want video on regularization ....plz 😄 😄 😄

[@pratikpande5917]

Thank you Sir, videos from this series helped me get a clear understanding about the concepts. Keep making such videos , they sure help a lot.

[@banditasahoo9663]

Very good explanation in short time... 👍👍

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5 Mins engineering, gate smashers and Sanchit sir are the life saviors 🌚🌚 lots of love....!!

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[@JyotiSingh-rz2gg]

Your vedio is very very much helpful for my samester exams. Thanku so much sir...

[@Anonymous-mz9un]

*Video. not Vedio.

[@bhavya2301]

Amazing Effort !! 😄 😄

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Yes sir...as time is less please only concentrate on important topics

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Very good ML content,people are giving so much money in ML course not looking in this content

[@MrDeepak8866]

why i didn't watched this before , very helpful .thankyou

[@sharadpkumar]

bhai maza aa gya....

[@jeniajeba7230]

very easily explained and easy to understand , amazing ! keep up the good work :)

[@SB_Roy_Vlogs]

Wow....nice

[@seducation9982]

Great explanation sir ..thank you sir ☺️

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[@saiful_not_found]

sabko pass karayega apna phaizal 5 minute mai

[@siddharthpatil1879]

Ek hi toh dil hai kitni baar jeetoge sir😂

[@vickyrajray2952]

THANKS MANNNNNN

[@madhushreearun1089]

Best and simplest possible explanation.

[@mohammedrehman4109]

Very Nice and precise explanation. You did a lot of home work on PCA in making precise. Thank you

[@Fatima-pp5ue]

good to hear such informative video

[@devr4j]

Love From IIT Dholakpur Sir

[@freetube7767]

Superb explanation.

[@nishiraju6359]

Nice content its really helps me alot ..... Request you to keep uploading videos .. more n more .. Once again thank you so much

[@girijaprasadpatnaik2113]

Love you brother 🌻🌻🌻

[@cybershrajal]

Sirrrrr firr aa gya mai .

[@hayatt143]

Great Video. Few points needs clarification like? 1. Why the only PC1 will b considered that means always 1 view is considered. 2. What is the view exactly? 3. How being orthogonal make them different? (Is it orthogonal properties ?) 4. (Most impt) Just by having different view how the features are reduced. arn't we still putting all the features to training? (This explanation was abstract. A bit technical would have done wonders.

[@minhaaj]

well done bro. kamal videos hein.

[@vedant6460]

Thanks a lot for this video💯💯💯💯💯💯💯💯

[@naeemchaudry733]

sir you deserve the nobel prize. your way of explaining is so amazing.

[@sudarshandev6369]

awesome means awesome explaination sir thanku so much

[@silparaniswain5492]

Mind blowing sir

[@l2mbenop346]

Superb Explanation !

[@sumeetkaur902]

Excellent Explanation, Thank U sir

[@ManuGupta13392]

preventing overfitting is a bad use of PCA. The main reason of PCA is better visualization, spped up the process/reduce memory - Source - Andrew NG machine Learning course

[@thedeepakmor]

thanx brthr it was very helpful you gained a subscriber

[@nalisharathod6098]

Please do a video in SVD (Singular Value Decomposition) . I really love your videos very useful . Thank you soo much

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[@gayathri5216]

Thank you so much sir...very useful video sir...😊

[@shashankparihar4984]

when you built a model using data, the model summary gives Rsq and Rsq(adj) values and difference in these values almost more than 20% tells that the model is over-fitting because as you keep adding the independent variables or attributes to the model Rsq value keeps increasing but if Rsq(adj) keeps reducing this means the added terms are not improving the model instead inflating the model. we can either drop those terms from the model which are not improving the model or we can perform principal component analysis by reducing the dimensions without dropping the attributes. basic rule to select principal components (PC) from all given principal components is to see the eigen values of these components, select those PC's whose eigen values are >= 1

[@rubina-hq3gc]

best video sir its help me a lot

[@manishn2442]

sir thank you very much, you explain very well.

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[@animationcrust1993]

Thank you sir ☺️🙏

[@aloktiwari1109]

Good and Easy explanation. You could clarify more why PC1 is selected over PC2, There is a reason for it.

[@Raag_Jhankaar]

Wahh Bhai wahh

[@datasciencewithshreyas1806]

superb video

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Thanks also from Agricultural side

[@weekendvibes468]

I understood everything thank you so much 👌👌

[@mohammadnafees9704]

great explanation

[@nikhilpachkor8349]

sahi bataya bhaiya

[@poojamankar]

Thanks for sharing.... Good efforts

[@parthprajapati3487]

It was nice and simple explaination

[@azmatsiddique3564]

❤️thank you sir..great explanation

[@azmatsiddique3564]

Can you please upload practical of pca

[@amritgurung1722]

He teaches whole CS courses!!!

[@MrSoumyabrata]

Thank you for explaining very well. I just like how simply you can explain any complex topic.