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.

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

At 4:43, W transpose will be a row vector. W itself is a column vector and you need to take a transpose of W to make it a row vector - that it [-1  0]. x is fine as its a column vector anyways. Your calculation of -4 for y=w(transpose)x will be correct for the row vector of W - that is transpose of the column vector W

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

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.

At 4:43, I think the W transpose will be a row vector. W itself is a column vector and you need to take a transpose of W to make it a row vector - that it [-1  0]. x is fine as its a column vector anyways. Your calculation of -4 for y=w(transpose)x will be correct for the row vector of W - that is transpose of the column vector W

THANK YOU !!!! I can not even explain how much you helped me, I was about to cry as i was not able to understand the math behind SVM and why we use Lagrangian function. I have exam after 10 days and your videos are really helping me in this time thanks once again and HAPPY GURUPURNIMA from bottom of my heart !!!

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.

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

Please Check that whether w1 is the slope of the hyper plane because if we consider the equation w1x1+w2x2+b=0, then the slope comes out to be -w2/w1 , so tehnically in the example you explained you took y axis to be your hyperplane

at 2:55, b is not equal to c, which is consider here as 'slope'. b is bias term which is not added to the feature vector when dealing with SVM.

wtx + b will be positive for the points above the line.

One correction. Here the line is y=-x(slope intercept form), in general form ax+by-d=0, where a=1 and b=1, and d=0 (slope = -a/b and intercept =d/b). Then while writing in dot product terms, the w(transpose)=row vector[a,b]=[1,1].

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...

How did you decide that equation would be wx+b=1 and wx+b=-1 for the marginal lines? It could be anything in place of -1 and 1 and hence the function that you have to maximize can change. please solve this doubt

Sir we need SVM regression

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.

I have a doubt, at 10.43, Isn't the maximum distance between margins be X1 + X2?  Thank you for this simplified lecture on SVM.

Hello Sir! Thankyou for this lovely video but sir at 3:20 i dont understand how can we get 1×1 as output after multiplication because as per the rule of matrices if wt has a dimension of 2×1 and x has a dimension of 1×2 then resultant matrix will be 2×2.....Please explain anyone

I sincerely thank you Mr. Naik that you have taught me ML topics very easy way that University Bern , Switzerland professors couldn't able to teach me properly. I'm originally from Bangladesh and i really appreciate your effort. May God Bless you and keep doing good works like this. Thank you once again.