PCA Indepth Geometric And Mathematical InDepth Intuition ML Algorithms

github Materials: github.com/kri...
Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science.

[@exploreEverything4519]

First I understood pca concept 3 years back from nptel lecture. It was full of mathematics and It went far above my head because the theory part was missing. Believe me with your explanations I can understand his lecture too. No one could explain the way you have explained. It was outstanding.

[@aditinautiyal4299]

Thank you so much for not only sharing your knowledge but also putting so much effort to cover each and every point of the particular topic.

[@salminsultana9654]

So I am your new fan sir. Today I was searching for videos about the auto eda. And I found yours. After that I started to watch your videos about pca clustering etc. And you are just amazing.. now I can understand properly. Thanks a lot sir.

[@aj_actuarial_ca]

PCA is so very well explained in your video sir. You're really the best teacher ever !!!

[@IshanGarg-y1u]

This is a good video, I recommend first you watch PCS step by step guide from stat quest to get a high level view with animations, then you watch this video to get more details and understanding alongside some code. Then in case you want to know the mathematics behind it refer to some articles online where the explain why we calculate the covariance matrix, then build the objective function using lagrange multiplier and then derive why eigen values of covariance matrix are the desired results

[@pritamrajbhar9504]

thanks a lot, Krish this is the simplest and most detailed video about PCA.

[@akashpaul9892]

You really are a good teacher brother... Teaching with relatable examples help to understand each topic so perfectly and easily.. Thank you so much brother.. Keep teaching us... Love from Bangladesh

[@man9mj]

thank you for this elegant effort in explaining PCA

[@Harsh_Yadav_IITKGP]

Krish your efforts are remarkable in this ml series.....

[@adnanshujah6230]

best of the best lecture .covers all the required concepts about subject . most of videos available only shows how to perform PCA but not whay it is required and concept behind it .but sir Krish thankyou so much for such a detailed lecture and clearing the concepts . highly recommended lecture and his channel 🥰🥰🥰🥰🥰🥰

[@adnanshujah6230]

i simply say this one video is enough to get the clear concept ;once again thankyou soooooo .... much sir Krish

[@syco-brain8543]

best video about pca on internet so far

[@SanthoshKumar-dk8vs]

Thanks for sharing Krish really helpfull, last two days am refreshing this topic only🤗

[@vinothkumar7531]

You are a great teacher I ever seen in my entire life.The way you are teaching even makes the lazy or slow learner to a strong learner using Krish Naik g(ji) Boosting algorithm.Just Kidding 😃😃.Hatsoff to your effort to help the people.

[@ashwintiwari9642]

No where I can find this explanation it's too good no confusion no complex demonstration use cases a cleanest and simplest way to understand PCA in depth thanks alot Krish it takes lot of takes and research to explain single topics in data science and in this way it's all appreciated work

[@taslima5007]

You are my favourite youtuber and teacher.

[@paneercheeseparatha]

Wonderful try to explain PCA without much mathematics. Though it would be great if you also do a video on implementing PCA from scratch in python. Loved your playlist! kudos to you!

[@dipamsarkar6626]

This guy should be named as "God father of Data Science India" an absolute legend

[@BhagatRaviNarayan]

He used the comparison operator in reverse order Accuracy1 < Accuracy2 < Accuracy3 > Accuracy4 > Accuracy5, Due to curse of dimensionality. 5:35

[@yogendrapratap1982]

Everything had been really resourceful in lecture series but this lecture was overly extended, 30 min topic has been extended to 1 hours 30 mins repeating same stuff again and again

[@amitx26]

Sir, I thing have felt strongly is that you expain and deliver a little better in recorded videos. Thanks for providing such great content for us for free!

[@RakshithML-vo1tr]

Hi bro I am starting data science how can I start? By seeing Krish sir roadmap and like u said should I prefer recorded videos

[@samareshms4591]

This guy is single handedly carrying the AI ML community in the India 🙇‍♂🙇‍♂

[@ramakrishnayellela7455]

Such a good explanation krish

[@kvafsu225]

Excellent presentation.

[@viratkumar9161]

Its quite vage to say if pearson correlation value is zero there is no relationship between x and y. Example consider Y= mod(X) line the person correlation is 0, but still there is relationship easily visible after plotting

[@SiddharthSwamynathan]

Correct. Pearson correlation has the capacity only to capture the linear relationship. Coefficient 0, would be no linear relationship exists. But there exists a possibility of a non linear relationship within the covariates and target.

[@pankajray5939]

PCA is one of the important topics of ML

[@irisshutterwork1411]

Well explained. Thank you

[@unicornsolutiongh2022]

powerfull lecture. keep it up sir

[@harshitsamdhani1708]

Thank You for the video

[@the-ghost-in-the-machine1108]

Thanks sir, god bless you!

[@AjayPatel-pc1yf]

Gjb sir mja aa gaya❤

[@mr.pianist]

very good lec beginner friendly

[@thop9747]

was really helpful. Keep up the work sir.

[@bhagyashriakolkar7763]

Thank you sir....nice explanation

[@manikandanm3277]

In theory part, to find the eigen values, you multiply the covariance matrix with a vector. How's that particular vector V is chosen and used to multiply with the covariance matrix? I'm confused with this only, otherwise a great lecture, thanks krish👍

[@priyam39]

That v is the eigen vector itself we are looking for.Sir just explained

[@meghananddussa62]

Covariance(direction) , correlation coefficient (strength and direction) both will tell about linear relationship only, they do not explain about non-linear relationship

[@baravind6548]

In extracting from 2D to 1D, if PC1 has the higer varience and PC2 has 2nd higher varience. Is it nessesary that PC1 should be perpendicular to PC2?

[@javeedtech]

Thanks for video, from fsds batch 2

[@yachitmahajan3579]

best explanation

[@IzuchukwuOkafor-v6e]

Very lucid explanation of PCA.

[@samthomas3881]

Thanks Sir!

[@BMVLM-]

Bhai content mast hai lekin advertisment bhot sare hai bot disturbing.

[@PAVVamshhiKrishna]

Fantastic

[@Nikhillllllllllllll]

how to get names of those 2 features we got after feature extraction

[@eurekad7340]

If possible could you please make video on truncated svd as well. I searched but I couldn't find any video on svd from you

[@faizannaseem3384]

See Go Classes Free Leactures for SVD

[@shivachauhan2837]

To improve my resume what should I try kaggle Or open source

[@theharvi_]

❤thx

[@BharatDhungana-n4s]

implementation is best

[@sumankumar01]

Campus x and you both refer same books or what since the example is same ?

[@CodeWonders_]

Can you tell me who will teach in data science course you or sudhanshu sir ?

[@chayanikaboruha6657]

Krish please make a video regarding how we can use auto encoder for text data

[@jitendrasahay3847]

If we have 3 features then we are getting 3 eigen vectors and later we combine 2 out of them to create 1 eigen vector. Combining here basically mean projection. Earlier when we projected we got n eigen vectors out of n feature then again we will get 2 eigen vectors. Where the dimensionality reduction is happening??? What I m missing here really??? Can anyone help ???

[@RahulA-b9o]

How do i know that the model is over feeded.. any method to find out that the model trained is under curse of Dimensionality???????

[@lagangupta3193]

How will we decide the number of features that we have to mention in n_components?

[@user-rx5kq6oo9y]

Bro can you make cheat sheet of data science like multiple dsa sheets on youtube?

[@MamunKhan-px2vb]

Just Great

[@MrKhan-xu1vf]

Kinda amazing teaching skills

[@ITSimplifiedinHINDI]

Greater than ko Less than aur Less Than ko Greater Than, kyoun likh rahe ho Guruji.

[@arungireesh686]

superb

[@Bitter_Truth-zc4eq]

Which software are you using for writing?

[@KRSandeep]

Scrble Ink which is available for windows laptop only

[@mohitkumarsingh7318]

Sir pls, also cover SVD , it's a request

[@viratjanghu945]

Sir please make a video on the independent component analysis and linear discriminant analysis it is my humble request sir please

[@ramdharavath7542]

Useful

[@shruti9731]

❤❤

[@SohanDeshar-pf6zh]

Good explanation but it might be a good idea to remove one of the "InDepth"s from the video title.

[@muhammadrafiq1720]

There is Ad after each 3 to 4 minets , difficult to concentrate especially with low speed inter et.

[@baravind6548]

How to get the vector v? that is to be multiplied by A

[@kunalpandya8468]

After we get 2 features from pca, what is the name of those two features?

[@mr.patientwolfx5984]

sir what do you think of guvi data science program? can i join.

[@AmmarAnjum-h2s]

Why sir you don't talk point to point things..repeating everything again and missing some stuff to talk

[@somnath1235]

What does the covariance and corelation decide ? Does covariance denotes how closely 2 features exist? And does corelation denotes whether the features are directly or inversely proportional?

[@saisrinivas3066]

covariance only describes the type of relationship whereas correlation describes the type and strength of the relationship between two numerical variables

[@bhargav1811]

Correlation is scaled version of covariance !!!! Range of covariance = (-inf,+inf) Range of correlation = (-1,+1)

[@Datadynamo]

Covariance is a measure of the joint variability of two random variables. It tells you how two variables are related to each other. A positive covariance means that the variables are positively related, which means that as one variable increases, the other variable also tends to increase. A negative covariance means that the variables are inversely related, which means that as one variable increases, the other variable tends to decrease. Correlation is a normalized version of covariance, it gives the measure of the strength of the linear relationship between two variables. It ranges from -1 to 1, where -1 is the perfect negative correlation, 0 is no correlation and 1 is perfect positive correlation. Like covariance, it tells you how two variables are related to each other, but it gives you a more intuitive sense of the strength of the relationship, as it is scaled between -1 and 1.

[@shanthan9.]

Good video but too lengthy

[@siddharthmohapatra7297]

Sir I want to ask ...I have no coding skills and background...bcom Background Can I do data science masters from pw skills ... everything will be taught from verry basics ???

[@rutvikchauhan1572]

You can do it, first learn python , then search data science cources on youtube and on various apps like udemy , coursera , swayam...... And enrolled on it......

[@siddharthmohapatra7297]

@@rutvikchauhan1572 I have enrolled in pw skills

[@anuraganand6675]

@Rutvik Chauhan how is you feedback of pw skills data science course?

[@akindia8519]

​@@siddharthmohapatra7297 hi can you please give us feedback of pw skills' data science masters program?

[@siddhisg]

greater than less than symbol though🥲

[@vaibhavyadav-w8g]

[@jitendrasahay3847]

I have to say : a very short precise material has been elongated irritatingly. Repetative statements...

[@satyapujari7731]

After every five minutes, there was an advertisement, which made it difficult to concentrate while watching videos.

[@rajdama205]

Use youtube vanced broo

[@satyapujari7731]

@@rajdama205 Great! 🤟

[@vishalgupta9620]

noob knows nothing

[@priyotoshsahaThePowerOf23]

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