Linear Regression | Basis Functions

[@sidjindal]

i love you. I've been pondering about how a "linear" model can overfit for so many days and now it's all beautiful again.

[@VictoriaOtunsha]

I come back to this video all the time, what a great explanation. Thanks for the breakdown

[@christophkrass6929]

Junge, was fuer ein starkes Video du da produziert hast! Besser koennte eine Arklaerung kaum sein, und dazu noch diese 1a Animationen :D

[@matheusmaldaner]

the best video i've seen on this topic, thank you!

[@nevillelam7999]

it's a concise and informative video! Recommend to watch

[@albertoprb1]

This is so well explained! Well done!

[@StavrosLoudis]

Very clearly explained. Great video!!

[@abdullahfarooq426]

whatayyy explanation 🔥

[@20000726]

Awesome!!

[@polarbear986]

very good visualization!! helps me a lot

[@perstarke1295]

Nice to hear!

[@CollegeCompanion]

Great one!

[@flueepwrien6587]

facken underrated

[@TheRaspberryPiGuy]

Great video and explanation, good to supplement understanding from the MLPR course at University of Edinburgh

[@perstarke1295]

Extremely nice to hear, thanks🙌

[@sagaradoshi]

Thanks for wonderful video. I have one question regarding gaussian basis function. So if an input data point has 3 input features then do we have basis function for each feature (i.e., three gaussian function one for each feature)? and if yes, what will be by mean and standard deviation of each gaussian?

[@arc42video]

nice intro!

[@perstarke1295]

Cheers :))