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support vector machine (SVM) is a form of supervised learning. This algorithm functions by classifying the information or data into separate classes. The data here is plotted as a point in n-dimensional space with each point being the value of a specific coordinate.

SVM is also known as the kernel trick. Here, the functions with low dimension input space are transformed into high dimension space. In simple words, the non-separable problem is converted into a separable problem. It classifies the data into hyperplane and maximizes the distance between different classes involved.

SVM is used for encountering both regression and classification challenges. SVM is classified into 2 categories namely linear and non-linear categories. In linear SVM the classifiers can be separated using hyperplane and in non-linear SVM, it is not possible to separate the classifiers using a hyperplane. The advantage of using a support vector machine is it provides classification performance, it doesn’t require any assumption and does not overfit the data. To know more about SVM visit https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47, https://machinelearningmastery.com/support-vector-machines-for-machine-learning/