Congratulations! You just learned two important things. Of these, the most important one is that as a typical machine learning operator, you will spend most of your time understanding and refining the data—exactly what we just did in our first tiny machine learning example. And we hope that the example helped you to start switching your mental focus from algorithms to data. Later, you learned how important it is to have the correct experiment setup, and that it is vital to not mix up training and testing.
Admittedly, the use of polynomial fitting is not the coolest thing in the machine learning world. We have chosen it so as not to distract you with the coolness of some shiny algorithm, which encompasses the two most important points we just summarized above.
So, let's move to the next chapter, in which we will dive deep into SciKits-learn, the marvelous machine learning toolkit, give an overview of different types of learning, and show you the beauty of feature engineering.