9.3.1 we learned about linear SVMs for dassifying linearly separable data, what if the data are not linearly separable, as in Figure 9.10?The good news is that the approach described for lincar SVMs can be extended to create nonlinear SVMs for the classification of linearly inseparable data (also called non- linearly separable data, or nonlinear data for short).The linear SVMs we studied would not be able to find a feasible solution here.In such cases, line can be found that would separate the classes.Now what?