Summary

In this chapter, we took our first big leap into constructing machine learning models and making predictions with labeled datasets. We began our analysis by looking at a variety of different ways to construct linear models, starting with the precise least squares method, which is very good when modeling small amounts of data that can be processed using the available computer memory. The performance of linear models can be improved using dummy variables, which we created from categorical variables, adding additional features and context to the model. We then used linear regression analysis with a polynomial model to further improve performance, fitting a more natural curve to the dataset, and we investigated other non-linear feature engineering with the addition of sine and cosine series as predictors.

As a generalization from explicit linear regression, we implemented the gradient descent algorithm, which we noted, while not as precise as the least squares method (for a...