In this chapter, we studied some of the most used techniques in machine learning. We created hypothesis representations for linear and logistic regression. You learned how to create a cost function to measure the performance of the hypothesis on training data, and how to minimize the cost function in order to fit the parameters, using both gradient descent and the normal equation. We showed how you could fit the hypothesis function to nonlinear data by using polynomial terms in the hypothesis function. Finally, we looked at regularization, its uses, and how to apply it to logistic and linear regression.

These are powerful techniques used widely in many different machine learning algorithms. However, as you have probably realized, there is a lot more to the story. The models we have looked at so far usually require considerable human intervention to get them to perform usefully. For example, we have to set the hyper parameters, such as the learning rate or regularization parameter, and, in the case of non linear data, we have to try and find polynomial terms that will force our hypothesis to fit the data. It will be difficult to determine exactly what these terms are, especially when we have many features. In the next chapter, we will look at the ideas that drive some of the most powerful learning algorithms on the planet, that is, neural networks.