Summary
In this chapter, we continued our journey of supervised learning with SVM. You learned about the mechanics of an SVM, kernel techniques, implementations of SVM, and other important concepts of machine learning classification, including multiclass classification strategies and grid search, as well as useful tips to use an SVM (for example, choosing between kernels and tuning parameters). Then, we finally put into practice what you learned in the form of real-world use cases, including face recognition. You also learned about SVM’s extension to regression, SVR.
In the next chapter, we will review what you have learned so far in this book and examine the best practices of real-world machine learning. The chapter aims to make your learning foolproof and get you ready for the entire machine learning workflow and productionization. This will be a wrap-up of the general machine learning techniques before we move on to more complex topics in the final three chapters.
...
