Visual Explanation of Principal Component Analysis, Covariance, SVD

Would love to request an in person version

Sexy

Excellent video, thank you!

Gotta echo the other comments here. Very succinct and understandable. You brought in the linear algebra without getting bogged down in it. Folks that don't have a strong grasp of that subject will still probably be able to get the main points of your presentation. Nicely done!

PS: Video is targeted at people who already have a deep knowledge of what the video is trying to explain.

thank you for this amazing video

I have always dreaded statistics, but this video made these concepts so simple while connecting it to Linear algebra. Thank you so much ❤

Why do you stop making videos?

I thought PCA was a hard concept. Your video is so great!

thank you for this amazing and simple explanation

Thank you! very nice video, well explained!

Great concise presentation, much appreciated! 👍

Good job, no wasted time

Awesome explanation!! Nobody did it better!

Around the minute of 1.36, you said "we divide by n for covariance", but we divide by n-1, instead. Please, do check on that. Thanks for the video. Maybe, I sohuld say estimated covariance has the n-1 division.

Very nice video. I plan to use it for my teaching. What puzzles me a bit is that the PCs you give as an example are not orthogonal to each other.

Thank you. It was beautiful

Great video! Can anyone tell how she decided that PC1 is spine length and PC2 is Body mass? Should we guess (hypothesize) this in real world scenarios?

Very Nice..pls keep posting

Good lecture

Graphical interpretation of covariance is very intuitive and useful for me. Thank you.

Good explanation

great explanation

I do understand that eigenvalues represent the factor by which the eigenvectors are scaled, but how do they signify “the importance of certain behaviors in a system”, what other information do eigenvalues tell us other than a scaling factor? Also, why do eigenvectors point towards the spread of data?

No one explains why they use covariance matrix. Why not use actual data and find its igen vector/igen values. I have been watching hundreds of videos books. No one explains that. It just doesn't make sense to me to use covariance matrix. Covariance is very useless parameter. It doesn't tell you much at all.

Believe it or not, I've been wondering a lot about the concept of covariance because every video seems to miss the reason behind the idea. But I think I kind of figured it out today before watching this video and I drew the same exact thing that is in the thumbnail. So I guess was thinking correctly : ))

thanks for this simple yet very clear explanation

babe var(x,x) makes no sense. either you say var(x) or cov(x,x)

Best PCA Visual Explanation! Thank You!!!

Plz do more videos

Great clarity. You clearly understand your stuff from a deep level so it's easy to teach.

poggers explination thankyou

Thank you, Ma'am!

Wow, that was quite good explanation.

beautiful, thanks a lot!

Great explication. Thank you.

Congratulations Emma, your work is excellent!

Hello Emma, Great job! Very nicely explained.

Great explanation!

nice job was always kinda confused by this.

I just love the voice🙄😸

Thank you for this great lecture.

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This video needs a golden buzzer.

Great video, thank you!

Very nice explanation!