Chapter 1, Introduction to Feature Engineering, is an introduction to the basic terminology of feature engineering and a quick look at the types of problems we will be solving throughout this book.
Chapter 2, Feature Understanding – What's in My Dataset?, looks at the types of data we will encounter in the wild and how to deal with each one separately or together.
Chapter 3, Feature Improvement - Cleaning Datasets, explains various ways to fill in missing data and how different techniques lead to different structural changes in data that may lead to poorer machine learning performance.
Chapter 4, Feature Construction, is a look at how we can create new features based on what was already given to us in an effort to inflate the structure of data.
Chapter 5, Feature Selection, shows quantitative measures to decide which features are worthy of being kept in our data pipeline.
Chapter 6, Feature Transformations, uses advanced linear algebra and mathematical techniques to impose a rigid structure on data for the purpose of enhancing performance of our pipelines.
Chapter 7, Feature Learning, covers the use of state-of-the-art machine learning and artificial intelligence learning algorithms to discover latent features of our data that few humans could fathom.
Chapter 8, Case Studies, is an array of case studies shown in order to solidify the ideas of feature engineering.
