Opettele peruskäsitteitä, joilla Support Vector Machine (SVM) muodostaa voimakkaan lineaarisen luokittelijan.1. Data Preparation: Before applying SVM, it's essential to preprocess your data. This includes normalizing or scaling your features and handling missing values.2. Linear vs. Non-Linear Classification: SVM can be used for both linear and non-linear classification. In linear classification, the decision boundary is a straight line, while in non-linear classification, the decision boundary is a curve.3. Hard Margin vs. Soft Margin: SVM can be applied with a hard margin or a soft margin. A hard margin tries to find a decision boundary that perfectly separates the classes, while a soft margin allows for some misclassifications