Dimensionality Reduction : Data Science Concepts

Why would we want to reduce the number of features ? And how do we do it ?
PCA Video : • Principal Component An...
LASSO Video : • Lasso Regression
My Patreon : www.patreon.com/user?u=49277905

[@NarekGevorgyan]

man you are a brilliant guy! you explain everything in very understandable language.

[@davidliau2336]

Thanks for the video -- great review material!

[@michaelprashanth664]

you are awesome, i am starting to learn scRNAseq and the application of ML in CompBio, this explanation was like a smoothie.

[@wvagner284]

Thanks a lot for such a great explanation!

[@trongang8914]

thanks, your explanation is real help

[@prathmeshlonkar2387]

I came, I saw, I understood, I liked, I subscribed!

[@ritvikmath]

Haha thanks!

[@negusuworku1871]

very helpful Thank you.

[@user-or7ji5hv8y]

Wondering can information theory / entropy be useful in this context as well for dimension reduction.

[@sivuyilesifuba]

Nice👏

[@philtoa334]

Thx.

[@JanLaalaa]

wow i got it in the first 30 seconds of the video

[@jameslucas5590]

I wish you had Method 3, FA. That would be a nice comparison of the three.

[@BeKindPlox]

Whats FA?

[@sunilmathew2914]

Factor Analysis 🧐

[@paulseidel5819]

I believe fa is the same as pca except the eigenvectors are rotated.