Basics of PCA (Principal Component Analysis) : Data Science Concepts
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- Опубліковано 14 жов 2024
- Gentle Intro to Principal Component Analysis (PCA)
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Finally, someone who introduces the problem first, the need, and then shows the solution. Most videos give you the solution and don't give you the problem at all, so you're left wondering why the thing exists in the first place. Great video!
The best comment I have read in forever.
Better than Josh Starmer
Really Great comment, coulnt agree more
@@azamatbagatov4933 ? Josh Starmer also does that
I don't know if you realize how good you are!
This "high level" explanation was just what I needed to get rid out of doubts about how to implement PCA. Thanks so much for your effort. You got 1 new follower.
Glad it helped!
Finally, a clear video on PCA for a guy coming from biomedicine.
Thank you!
You have a rear gift of explaining things in a very clear way my friend. I’m curious to see the next video!
Thank you very much!
I started teaching business analytics and find your teaching method as very consumable. Assuming length and weight of kitties is correlated I assume you could have just one PC.
thanks for explaining it in plain english before going into some crazy math. I appreciate this
Finishing a degree during COVID-19 and this man has explained better in 6 minutes what a lecturer I'm paying £9,000 spent 3 different 1 hour teams meetings trying and failing to explain
One of the best channels which go in depth while still keeping it clear. Thank you for creating such content!
and not bore you with the math, if you don't need it.
This was outstanding. Exactly what I needed.
You just saved me a lot of time and stress with this simple straightforward film. Thanks a lot.
Please make another one like this on the fisher-face and LDA algorithm. 😇😇😇
Glad it helped!
Great vids!!! My question is to everyone including the creator: Is there any suggestion as to the order of videos I may want to watch them? Or these are just for completion of knowledge, where we watch just ANY topic that we need to know more of, like an encyclopedia?
Hey Ritvik,
I might be your biggest fan! Whether I'm cramming for an exam, trying to understand a paper, or just killing time on a curious Saturday, I always look for your videos first!
That said, it would be very helpful if you could include a link to part 2 in your description or at the end of the video.
Thank you for all you do :)
Great video! Thank you for sharing.
Thanks man, i really need this kind of video to understand the topic
looked a bunch of videos, but only this explained me well the interest of PCA !
fantastic video, the examples and using real numbers really clarifies things tysm !!
All the great reviews below are understatements ;-)
This was excellent.
Great video! Clear and well explained. Really appreciate it. Thank you
Glad it was helpful!
Very insightful
Awesome work man! Thank you and God bless
Really like your explanation of PCA! I am a food microbiology Ph.D. student. Thank you for this great video! Hope more people can see this！Cheers！
Glad it was helpful!
When we study, our aim is to get clarity where we will use this concept, you explained it very well. Thank you so much.
Nicely explained. Thank you.
Bro. your videos are amazing and we really appreciate your work. But we are dependent on your channel so can you structure your content into bins of playlists? Or may be just make one playlist but arrange all videos in proper sequence.
Absolutely genius. I was looking for an intuitive understanding and here you se brother.
Can you request you to do a few videos on locality preserving projection?
Very well explained. Thanks
Glad it was helpful!
You are a genius!
Amazing content. Could you please order the playlist on your channel for beginners ?
this video helpt me a lot thank u
This probably hurts me more than it should, but he means purr volume instead of purr frequency. Purr frequency is either the amount of times a cat purrs, or the tonal frequency on which the cat purrs. Yes I'm fun at parties.
love it!
Thank you.
Can you do a video comparing V.I.F and PCA and LASSO/RIDGE regularization. They seem to deal with very similar objective of multicollinearity.
Excellent explanation as always.
"We are cat researchers" *Instantly likes*
:D
Thank you for the video, Ritvik! Not related to PCA but I was wondering if you could make a video on latent dirichlet allocation (lda) for topic modeling? It would be really helpful.
How would you compare PCA vs Variable Selection/Regularization (Lasso/Ridge/Elastic Net), Is there any video comparing both?
Liked and subscribed
Very interesting thank you
Glad you enjoyed it
@@ritvikmath I'm really enjoying your videos because it's giving me a working understanding of the mechanics under the hood. I've been toying with data science for years and struggled quite a bit, but with videos like this and the aid of AI bots to help (particularly with tracing errors), I'm learning so much faster today. As you get into great detail across the whole spectrum of machine learning I wondered if you had a video or might consider making one to show the structured approach to a machine learning project. Looking at the type of data the desired outcome and then piece-mealing all of the tools and approaches to the strategy. Each of your videos is very holistic, I would love to see one that was holistic for a whole machine learning project, without needing to re-explain the detailed concepts held in your other videos.
We are saying that we are reducing the dimensions but each PCA is a linear combination of all the attributes. So how is this reducing dimension? This is the part I am not grasping.
Great explanation, thank you!
You are awesome dude
Terrific video by the way. Do you mean you loose a little bit of data or do you mean you loose a little bit of information? I am guessing that what you are saying is that you loose data without loosing information.
great explanation
how can i know, how much every PCA component capturing data ?
Frequency of purr =/= Amplitude of purr
I was having a very bad day and this showed up!
*Thanks UA-cam*
how is dimensionality reduction & feature extraction different?
does PCA solve the issue of multicollinearity?
"A little messy, but it says 'feature extraction'."
Agree to disagree.
Imagine studying ONLY weights and lengths of cats for TEN years
You say your cat dataset has 3 Dimensions where as one could be left out because it has no great significance. I know it is a toy example but why wouldn't you just leave it out without calculating it with PCA?
Great question. In this case when it is easy to visualize in 3d, we can just leave out that dimension. But, when the dimensions get higher and it get's harder to visualize you might consider PCA. The bigger reason is that sometimes the points all lie on a line or plane but that line or plane does not line up perfectly with the axes (one variable is a linear combination of others) so PCA is needed.
To all the ppl who care about cats HAHAHA nice one
Are you Indian?