This book is not an introduction to ML. There are many books, courses, and resources available for ML theories, and therefore, this book shies away from these concepts to focus on the practical aspects of ML. You don't have to know these topics inside out--for concepts whose exact definitions can take some effort to remember, e.g., F1 score, we include short notes as references--but you should have a rough sense of what they mean going in. While this book mentions current tools to illustrate certain concepts and solutions, it's not a tutorial book. Tools go in and out of style quickly, but fundamental approaches to problem solving should last a bit longer.

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available.