In recent years, Machine Learning techniques, such as Support Vector Machine (SVM), Radial Basis Function (RBF) network, and so on, have been widely employed in engineering fields. In particular, the SVM is one of the powerful classifiers. However, the formulation of the SVM is slightly complex. In addition, we have to solve the Quadratic Programming (QP). The Least-Squares Support Vector Machine (LS-SVM) is one of the machine learning techniques. In this paper, the LS-SVM is introduced. In the LS-SVM, equality constraints is considered. Thus, the solution can be obtained by solving a set of linear equations instead of solving QP problem. The extension of the classical SVM to the SVR is more complex because the epsilon insensitive loss function is introduced, while it is very easy to extend the LS-SVM classifier to the regression version.