Support Vector Machines (SVMs) are a supervised machine learning algorithm that can be used for both classification and regression tasks. SVMs work by finding the hyperplane that best separates the two classes of data. The hyperplane is a line or a plane that divides the data into two regions, with each region containing all of the data points of one class.

2-Minute crash course on Support Vector Machine, one of the simplest and most elegant  classification methods in Machine Learning. Unlike neural networks, SVMs can work with very small datasets and are not prone to overfitting.

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