Support Vector Machine Algorithm

This is a must need to know algorithm for everyone in this field, it is not covered in the specialization so I thought I can write a post about it just to make sure a lot of you know about it. so what is Support Vector Machine Algorithm?

Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. Though we say regression problems as well its best suited for classification. The objective of SVM algorithm is to find a hyperplane in an N-dimensional space that distinctly classifies the data points. The dimension of the hyperplane depends upon the number of features. If the number of input features is two, then the hyperplane is just a line. If the number of input features is three, then the hyperplane becomes a 2-D plane. It becomes difficult to imagine when the number of features exceeds three.

Check out this link for details: Support Vector Machine Algorithm - GeeksforGeeks

Also this is a great lecture that covers everything you need to know about SVM: (154) 16. Learning: Support Vector Machines - YouTube


For anyone reading this, have a look at this explanation of why support vector machines (SVMs) are left out of the updated machine learning specialization.

Yes, but we still have to know about it.

I’ve been asked about it many times in interviews.