K Means ++ for Initialization || Lesson 106 || Machine Learning || Learning Monkey ||

[@sanhatahir8923]

Best explanation I've seen so far; so helpful!

[@lima073]

Complete and simple explanation, thank you very much!

[@mansoorbaig9232]

He nails it perfectly! So easily explained. Thanks.

[@giovanni_ferreira]

Really great explanation, right to the point of the algorithm. Thanks!

[@LearningMonkey]

Welcome. Have a great learning

[@akshathabhatpervaje367]

The Explanation is perfect, Thank you :)

[@rohitpant6473]

Thanks for sharing your knowledge I was triying to get this concept on various blogs but was getting congused.. You made it simple for me... Thanks to you

[@LearningMonkey]

Have a great learning

[@saketnandan4969]

thanku very much , crisp and clear explanantion

[@LearningMonkey]

Welcome

[@mata4125]

You made it very simple to understand, thanks !

[@aletieswar5819]

Simple and great explanation!

[@romedutechteam4202]

Very Good ! Explanation . Perfect Teaching!!!

[@LearningMonkey]

Thank you have a great learning

[@storiesshubham4145]

Whenever I get confused about Kmeans++...I always watch this video

[@viveksinha971]

Great explanation. Keep up the great work.

[@goldiusleonard8970]

Very clearr explanation!! Love your work man. Keep it up and appreciate it :)

[@redforestx7371]

Amazing as always. Much appreciated man!

[@suprithabhandary2940]

Wonderfully explained thank you

[@muhammadsaqib2961]

Quite clear explanation of condition ....

[@naveenys8404]

Thank you for your wonderful explanation of K-Means++

[@LearningMonkey]

Thank you. Have a great learning

[@bvsrevanth6856]

Really loved your explanation !

[@LearningMonkey]

Thank you. Have a great learning

[@arjunsherpa3826]

thanks for the clear content...

[@LearningMonkey]

Glad it was helpful!

[@hassanalimohammadi4553]

Thank you vry much. You explained it very well. :)

[@LearningMonkey]

Welcome

[@shobhitsrivastava9112]

Thanks for the clear explanation.

[@LearningMonkey]

Welcome

[@vidyachandran730]

Hi..I have a doubt regarding the selection of the third centroid.whether we take max distance from centroid 1 or centroid 2..Thanks in Advance

[@LearningMonkey]

Nearest centroid distance is calculated. Probability is proportional to distance

[@anishdhane1369]

Thanks alot!!🤩

[@tusharsalunkhe7916]

Thanks for the lecture Please correct me if I am wrong for following things- 1)Incase of K Means Clustering, Centroids(Initial or any further) may or may not be from our original data points. 2)Incase of K Means ++, apart from 1st Centroid other k-1 Centroids(initial) are always selected from our original data points.

[@LearningMonkey]

Hi tushar. Yes u are correct.

[@tusharsalunkhe7916]

Ok thank you

[@דניאל-ט9ד]

great explanation thank you

[@samuelsuther1583]

For step 3, instead of leaving it to probability, can we just select the data point that is furthest away from all the previously chosen centroids as the next centroid?

[@LearningMonkey]

Yes. But why to write another logic during implementation. Not much effect with that condition. Have a great learning

[@himanshumangoli6708]

Good morning sir, what about outliers, becoz outliers have more probability of getting selected due to distance parameter which we have taken for finding probability

[@LearningMonkey]

Even if you found outliers. After applying K means clustering we get centroid for cluster

[@thilokeshjain6767]

Good one sir

[@shivamrai7838]

nicely explained bro :) thanks

[@LearningMonkey]

Thank you. Have a great learning

[@lazyluke2773]

Good man

[@arwaalrifai]

thank you very much :)

[@LearningMonkey]

Welcome

[@Arjun147gtk]

made it crystel clear.

[@ankurpatel9670]

helpful

[@ahoangdinh3690]

wish have subtitle