Summary

In this chapter, we took a deeper dive into supervised learning, with a focus on regression modeling. Here, we discussed the difference between simple and multiple linear regression and looked at some important evaluation metrics for regression modeling. Then, we rolled up our sleeves on our case study, helping our company build a working regression model to predict the salaries of new employees. We carried out some data preprocessing steps and saw the importance of normalization in our modeling process.

At the end of the case study, we successfully built a salary prediction model, evaluated the model on our test set, and mastered how to save and load models for use at a later stage. Now, you can confidently build a regression model with TensorFlow.

In the next chapter, we’ll take a look at classification modeling.