K Nearest Neighbors | Intuitive explained | Machine Learning Basics 

More of these Please? Its super awesome and super intuitive. Specially do one on Linear, Logistic and SVM. Maybe include ensemble too.

I must admit the way the algorithm KNN is explained is phenomenal. I have struggled to understand how it conceptually works, but thanks to this video I finally understand it 100x better, definitely with some visualisations. Please, we need more content like this, so keep it up, man. 😁

MANNNN!!!💐💐💐 I came running from Reddit!! Your visualisations are AWESOME! (I am going to copy paste this comment on all of your videos!!) Please post more videos! THEY'RE A SERVICE TO HUMANITY!😅

Amazing, concise, intuitive video. Loved the code snippet too. Thank you!

Here from Reddit also. Crystal clear explanation, and I loved the coding example ate the end! Made everything click in my mind! Keep up the good work! Cheers :D

Extremely well taught; thanks a ton for the effort!

really well made, good work. I would actually prefer longer, more in-depth videos like this. Seeing the python code was super helpful!! I've just started learning machine learning but my professors are no good.

Simple and Clean Presentation, thank you!

Good job! I'm looking forward to watching more of your videos.

The video is visually aesthetic. This video is so underrated though, nice video

Thanks man, this helped me with my Machine learning homework, love u

Simple, elegant, clean

Oh wow. Those videos are very well made ! I already took a course on the topic, but I still envoyed watching this

Your content is great my friend , I am starting my ML journey ,gonna watch all your videos.....

Great video. Can’t wait to see more!

Way to go.. it's simply awesome..

Wow. This is neat!

I feel amazed why such kinds of RU-vid channels are not getting the subscribe they deserve......

Great video and excellent explanation!!

Quick and great explanation.. Thank you ❤👍

From Reddit, good explanation!

Thank you!❤

Supper Awsome Videos !! Please do more of these kinds. Thank you !!😁😁

Excellent content and great way to present it, need more of a kind.

What a piece of art

You are too good. Please more and more intuitions.

very clear explanation!

Here from reddit super rad explanation only thing is I would’ve added some form of test batch size so you can show the accuracy of it for funzies

yout visualization so good!

This was beautiful

Very clear e beautiful video. How did you make the video? What software did you use to produce the animations and the presentations? Congratulations!!!

This is really really good. Please do one on SVM and PCA please.

Keep them coming!

How does this channel not have 100k subs at least,

*Could you please tell me how the initial classification is done in KNN? I understand how a new value will be assigned to a category but how is the initial classification made?*

Thank you!!

Great vídeo. Keep It up!

Awesome animations!

So cool

The quality of your visuals is on a similar level as the very good math's focused 3Blue1Brown channel. 👌🤓

ay that was pretty crispy and straightforward. I'm about to try applying KNN for a homework task but is it really THAT simple?

In the above classification for k=7, what if both the values are same?

Great simple explanation!

This is amazing nice explaination ever tq Which software is used for video animation and editing

Amazing 👏🏻

Hey thanks for the video. I was wondering how you made these animations

helps a lot!!!!!!

This is awesome thank you

Reddit gang. Also followed you on IG for daily know-how.

Awesome. Please create more such videos

Cool! What tool for presenting are you using?

👍

Do we need to sort the distances before averaging?

At least the graphics and animations looked nice. The only thing I got from this video was what KNN is for

What is the main difference between KMeans clustering and KNN and their applications?

What are situations where we use Manhattan distance ?

How did he plot this using matplotlib

goat explaination

Subbed ya

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nice

please make video on all ML,DL Algorithms.