A Support Vector Machine (SVM) is a classification algorithm, typically used for binary classification. An SVM with gaussian kernel has been consistently shown to be one of the best machine learning models, achieving the highest accuracy in a large variety of datasets, especially datasets which do not involve images or audio.

Understanding the various hyper-parameters of an SVM are central to achieving high accuracy. In this tutorial, we'll learn what an SVM is, and what is the purpose of its various hyper-parameters.