Preface

The .NET Framework is one of the most successful application frameworks in history. Literally billions of lines of code have been written on the .NET Framework, with billions more to come. For all of its success, it can be argued that the .NET Framework is still underrepresented for data science endeavors. This book attempts to help address this issue by showing how machine learning can be rapidly injected into the common .NET line of business applications. It also shows how typical data science scenarios can be addressed using the .NET Framework. This book quickly builds upon an introduction to machine learning models and techniques in order to build real-world applications using machine learning. While by no means a comprehensive study of predictive analytics, it does address some of the more common issues that data scientists encounter when building their models.

Many books about machine learning are written with every chapter centering around a dataset and how to implement a model on that dataset. While this is a good way to build a mental blueprint (as well as some code boilerplate), this book is going to take a slightly different approach. This book centers around introducing the same application for the line of business development and one common open data dataset for the scientific programmer. We will then introduce different machine techniques, depending on the business scenario. This means you will be putting on different hats for each chapter. If you are a line of business software engineer, Chapters 2, 3, 6, and 9 will seem like old hat. If you are a research analyst, Chapters 4, 7, and 10 will be very familiar to you. I encourage you to try all chapters, regardless of your background, as you will perhaps gain a new perspective that will make you more effective as a data scientist. As a final note, one word you will not find in this book is "simply". It drives me nuts when I read a tutorial-based book and the author says "it is simply this" or "simply do that". If it was simple, I wouldn't need the book. I hope you find each of the chapters accessible and the code samples interesting, and these two factors can help you immediately in your career.