Maths Intuition Behind Support Vector Machine Part 2 | Machine Learning Data Science

In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
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[varuag]

I'm glad someone like you decided to make a video on this. I have found that many find SVM hard to grasp because they dive directly into the code without understanding the intuition behind it. This goes a long way in helping people out.

[myeschool]

My Dear Teacher
From my heart, I salute you, cause you to work so hard for us, your students, to teach the things with so much clarity. Praying for you.
....Noushad Rahim, Kerala

[victor]

I cannot begin to thank you enough for breaking down and simplifying the math behind the machine learning algorithms. Understanding the math under the hood is pertinent to tuning the hyperparameters. I love your videos and I'm always recommending aspiring data scientists to check out your channel.

[abhijitbhandari]

You really are too passionate about teaching sir. Sometimes you even are being breathless, that excitement of teaching..got no words. Hope you will upload videos on topics related to DNN as well. Proud to be learning from you

[YouTubelesss]

man how could you remember all this... I keep forgetting the concepts after few weeks and had to watch it back to get a grasp on it.
A million thanks for you in sharing your precious knowledge with us.

[mahikhan]

a sensible tutor i have seen my life who always traces students pulse . there are lot of tutorials about svm in youtube but no other covered A to z as like as krish . appreciate u krish

[akshaykhavare]

The way you simplify things is really commendable. After reading lot of blogs and going through other resources finally landed here and it was worth it. Thank you Sir.

[datahat]

Very informative video and simple to understand. A slight oversight error, as the X here is 2-d [x1, x2], W (without b) must be 2-d as well [w1, w2]... If we consider bias b, then X = [x1 x2 1] and W is [w1 w2 b]... and in such a case we shall have a plane instead of a line

[prashantkumarvishwakarma]

Thanks Sir, For all of yours video agr aap nhi hote to kbhi v itna sikh nahi pate humare instute wale log sirf overview bata k chor diye but real knowledge to aapki video se Thank You So Much

[usamaahmad]

Thank you so much Sir, the way you were teaching and i was getting all of your points, my love for your method and dedication starts hiking, a lot of love, respect and salute from Pakistan... knowledge have no boundries...

[jbhsmeta]

Just Great !!! Wow!!! - It was a great experience. Eagerly waiting for the part 3 of SVM covering the kernel trick.

[rvg]

The Regularization Parameter C is basically how much we want to avoid misclassification of points. If C is very large (infinite) we get the perfect classification of training samples smaller margin is considered, but if C is very small(0) it will cause the optimizer to find the maximum margin classifier even though it misclassified some points. Hence we have a find a good value of C in between.
The Gamma parameter defines how much influence a training example has. For example, if gamma is high only the nearest points from the margin are considered for calculating distances, but if gamma is low even farther points from the margin are also considered.

[wenqichen]

I can't wait to express my infinite appreciation for you, sir! This video is so so so intuitive and uses less advanced math!

[SumanBhartismn]

Abhi tk kahan the sir, I was searching a teacher like you in ML. Finally mission completed. Love from my side.

[rajraji]

Thank you so much Bro and I like you so much bro and Your individuality is seen through everyday and every videos I will become a data scientist one day ...

[kirushikeshdb]

The w matrix should be [1 1] because the line equation is x1 + x2 =0, also while computing the value of y, wT should be a dimension of 1x2 and X should be 2x1, so that you will get a single value.

[darshitsolanki]

Kernel have sigmoid s shape graph n linear and polynomial form
I m from statistics degree u have grt knowledge dude keep it up

[andrewwilliam]

Hey Krish,

I just want to say that your explanations are superb. I am new to Machine Learning and I took an online course about it but it barely gets into the mathematics. I understand that to get good and serious at ML we need a solid mathematical understanding of the various models, so i appreciate these videos that go in depth.

To be honest I watched it the first time and didn't completely get it, but I'm going to watch it again now!

[rupeshsingh]

Very very impressive explanation.. Thanks a lot. Bhagwan aapko hmesa khus or swasth rakhe....

[sivareddynagireddy]

I saw so many articles about svms, every one say directly distance formula simply maximize, but your simplification from strach is awesome sir !!!!