Chapter 3. Feature Extraction and Preprocessing

The examples discussed in the previous chapter used simple numeric explanatory variables, such as the diameter of a pizza. Many machine learning problems require learning from observations of categorical variables, text, or images. In this chapter, you will learn basic techniques for preprocessing data and creating feature representations of these observations. These techniques can be used with the regression models discussed in Chapter 2, Linear Regression, as well as the models we will discuss in subsequent chapters.

Extracting features from categorical variables

Many machine learning problems have categorical, or nominal, rather than continuous features. For example, an application that predicts ...

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