Mathematics of Support Vector Machine | Soft Margin SVM

[@abhishekkumar-hm6yp]

You explain it soo awesome.. I came here after watching udemy foreign lectures and there is a huge difference between explanations. Proud of Indian youtubers 😁

[@yusufkhanvlogs9581]

No one teach like u in youtube platform....extremely admirable for hardwork...thanks for making videos

[@sanaayakurup5453]

Dont have words to express how brilliant this SVM explanation is!

[@tayyabali1716]

Lots of love for you brother. You explained very well. Please continue these series just like Quadratic Programming, The Dual Problem, Kernelized SVMs, and Online SVMs. I believe your channel would have more subscribers who really wants to learn. You are fantastic.

[@Ujjayanroy]

most underrated video ever...hats off brother!!

[@shubhgajjar8782]

Your explain is so awesome bro👏🏻 than many IITs and NITs teachers Briallian!!

[@mohsinkamal2638]

ammazing explanation. you made it so easy. Watched so many videos on loss function. but the concept got clear after watching this lucid explanation. Thanks.

[@dheerajmupparam1583]

The best explanation RESPECT!

[@mihirnaik3383]

Thanks for breaking this algorithm into several videos. That way it's easy to understand and relate things. Great content as always!

[@AkhilKumar-ys3ph]

big fan sir..you should have 1 million subscribers

[@ujjwala7286]

Thanks a lot.You have explained very clearly and in simple language

[@rambaldotra2221]

The End!!!! Wow what an explaination !

[@riit1564]

great explanation. underrated videos on SVM

[@sukhwindersingh9268]

i love and miss the background sound of birds, watching the video from Canada. Thanks for the brilliant content.

[@sohamgosavi6797]

Great video dude. Keep up the good work

[@RNDbyvaibhav]

Thank You bhaiya
. ❤

[@sargamgupta7134]

Amazing explanation !!!

[@JordinainMared]

Good morning man , but how you can calculate the w,b based on the decision boundaries?

[@cse_042_shivbamsrinibasbho7]

very much amazed god bless you sir

[@sahirmohammad9027]

12:55 Sir isnt it L1 regularization? we're not squaring right?

[@jiyabyju]

thanks alot..you did really hardwork..

[@PoojaSharma-sr3fo]

You explain very well.pls explain above query I am not understand that

[@arun5351]

Great job !!!

[@pooja5300]

awesome.......

[@wanderlust4067]

You are best brother ❤

[@rockykumarverma980]

Thank you so much sir 🙏🙏🙏

[@khushimuddi7337]

great explaination

[@nightingale180]

I hope sir u get well soon

[@sandipansarkar9211]

finished watching

[@narendraparmar1631]

Thanks for this explanations

[@saurabhgarg5297]

Good explanation.

[@swenyyu560]

hello, can you please indicate the constraint within the soft margin svm? or it it the same as the hard margin svm model? thank you very much!

[@joeljoseph26]

Can you add eng subtitles please.

[@ParthivShah]

Thank You Sir.

[@AnkitKumar-sz3by]

well explained

[@ayu1323]

amazing bro

[@chandramohan7176]

u are awesome man:-)

[@n.kranjan1573]

sir…what is difference between agrmax and max.

[@mohamed-rayanelakehal1324]

thank you, if it is possible to add subtitle would be great....

[@adityabhatt04]

Bhai Andrew Ng se zyada acha explain kiya hai aapne.

[@AmanSharma-xq8tc]

Thank you sir

[@theotherside4918]

Very nice

[@vijaybhatt8060]

amazing

[@PoojaSharma-sr3fo]

How can max f(x) equal to min 1/ f(x)

[@WIN_1306]

coz it will be so

[@mayanknetam6532]

actually it is talking about x , means the x for which f(x) is maximum, for that same x , 1/f(x) will be minimum. Hope it helps

[@PoojaSharma-sr3fo]

I got my answer

[@debjitkarmakar5523]

Noodle bn gya ye🤣🤣

[@himanshigarg1431]

thank you sir