How k-nearest neighbors works

Learn more: e2eml.school/221
at the End to End Machine Learning School

[@3rdman99]

Finally videos about ML explained by somebody whose English I can understand.

[@waseemahmed4995]

Very clearly explained. And the examples were very well and appropriately chosen. Thanks Brandon!!

[@BrandonRohrer]

Thanks Waseem!

[@youtubecurious9000]

Nice explaination. Can you do more videos on other ml algorithms?

[@haswanthaekula7656]

This is the best video I have ever watched regarding KNNs. I just have one question though, what did you exactly mean when you said 'Learned feature scaling'?

[@BrandonRohrer]

Thank you Haswanth! Not to be too coy, but we walk through the details of this in the course (e2eml.school/221). I didn't include it in the video because 1) it's a pretty specialized rabbit hole and 2) there is no standard way to do it that I'm aware of. In the course we pull together a workable method that resembles Powell's Method for optimization, which boils down to iteratively making small changes to the weights and keeping the changes that result in an improvement.

[@zainulabideen_1]

Very very useful

[@zainulabideen_1]

Thank you

[@PanagiotisFoufoutis]

Great video, Love the comparison to GPT ;)

[@RajaSekharaReddyKaluri]

Nice. Thank you.

[@Justin-zw1hx]

WOW, you are LDS!

[@TiagoTiagoT]

Maybe it's not trainable in the conventional sense; but you still gotta tune the hyperparameters to obtain more accurate results, which could be interpreted as a form of training.

[@whannabi]

A form of tuning

[@RajaSekharaReddyKaluri]

How to feature scale categorical variables?

[@BrandonRohrer]

If you first convert the categorical feature to a one-hot representation (say, 0 for indented and 1 for rounded) then you can choose a scaling factor to multiply that by. That's a trick we step through in detail in Course 221 (e2eml.school/221).

[@victoraguirre7486]

Hot damn this video is soo goood