In a previous couple of articles, we started exploring some of the basic machine learning algorithms. We covered some simple regression and classification algorithms. We also used other technologies like TensorFlow, Pytorch and SciKit Learn for the implementation and application of these algorithms and we used optimization techniques such as Gradient Descent. In this article, we focus on one very powerful algorithm – Support Vector Machine or SVM. SVM is one of the most popular machine learning algorithms and for a good reason. This algorithm proved over and over again to be really good for both – classification and regression and every machine learning engineer should have it in their toolbox. It is also applicable to linear and non-linear data. Before we dive into details and implementation of this algorithm let’s see datasets and libraries that we use in this article.

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