Hands-On Transfer Learning with Python

Working with numerical data

Numerical data, commonly available in datasets in the form of integers or floating-point numbers and popularly known as continuous numerical data, is usually a ML friendly data type. By friendly, we refer to the fact that numeric data can be ingested in most ML algorithms directly. This however, does not mean that numeric data does not require additional processing and feature engineering steps.

There are various techniques for extracting and engineering features from numerical data. Let's look at some of those techniques in this section:

• Raw measures: These data attributes or features can be used directly in their raw or native format as they occur in the dataset without any additional processing. Examples can ...