Understanding Principal Component Analysis

Hi guys! In today's video, we are looking at some intuition behind principal component analysis. We will be examining the way principal components are calculated, and why they are calculated that way.

Shoutout to Shad Sharma and his amazing linear transformation visualizer inspired by 3Blue1Brown!

Here is a link to the notebook I used in the video:

Thanks so much for watching! If you have any questions, be sure to leave them in the comment section below. If you liked the video, don't forget to subscribe and hit the bell for more content!

See you all next time!

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[michaelaschlosserova]

Hey Gabriel, I just wanted to say thank you for putting such a valuable content out here! Just working on my final course assignment and your videos are a great inspiration and a guide that I come back to. You deserve way more subscribers, although I get that this channel is very niche.

[SonuKumar-vbnq]

I was waiting for tor this series of ML algo concepts. Thanks Gabriel

[swapnilchavan]

you are the Great Person and you simply way to explain each topic . you are
the fascinating Person

[SuperNayaab]

@7:35, how did you write on 2 different lines at once?
Is that possible on jupyter/colab notebooks ?

[thangld]

I don't speak English very well. But now, you who help me learn a good knowledge for a newcomer like me. Let's continue. Thank you very much

[Mohamm-ed]

Very informative video thank you for your effor.

[ajayforest]

Can we perform PCA for ranking data. I have data set of 250 individuals and 8 variables. Each individual was asked to rank these variables from least preferred (8) to most preferred (1). Is it possible and sensible to apply PCA on this type of data?