Support Vector Machines explained with Python examples

Support vector machines (SVM) is a supervised machine learning technique. And, even though it's mostly used in classification, it can also be applied to regression problems. SVMs define a decision boundary along with a maximal margin that separates almost all the points into two classes. Support vector machines are an improvement over maximal margin algorithms. Its biggest advantage is that it can define both a linear or a non-linear decision boundary by using kernel functions.