In this chapter, we were introduced to vectors, magnitude of vector, and the dot product. We learned about SVMs that can be used for both classification and regression. We studied support vectors and kernels and the different types of kernels. Lastly, we studied the SVM example and parameter optimization through grid search.
In the next chapter, we will learn about performance in ensemble learning.