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Support vector machine (SVM) is a supervised machine learning algorithm which is considered effective tool for both classification and regression problem. In a simple word, SVM tries to find a linearly separable hyperplane in order to separate members of one class from another. If SVM can not find the hyperplane for a given data set, it applies non-linear mapping to the training data and transform them to higher dimension where it searches for the optimal hyperplane. The SVM algorithm uses support vectors and margins in order to draw these hyperplanes in the training data. Since it has ability to understand the complex relation in input data by applying nonlinear mapping, it has high accuracy compare to other supervised classification algorithms (kNN, NCC..) People have been using SVM for different applications like: text data classification, image data(handwritten) recognition and more.