In recent years, engineers and executives have been attempting to implement machine learning (ML) and artificial intelligence (AI) to solve problems that, for the most part, have been solved using fairly manual methodologies. A great example would have to be advancements in natural language processing (NLP) and more specifically in natural language generation and understanding. Even more specifically, we point to AI systems that are able to read in raw text from a user (perhaps a disgruntled user of the latest smartphone) and can articulately and accurately respond with the prose of a human and the speed of a machine. In this chapter, we will be introducing topics of feature engineering, such as:
- Motivating examples of why feature engineering matters
- Basic understanding of machine learning, including performance, evaluation
- A detailed list of the chapters included in this book
