Preface

Machine learning is a very broad topic. The following quote sums it up nicely: The first problem facing you is the bewildering variety of learning algorithms available. Which one to use? There are literally thousands available, and hundreds more are published each year. (Domingo, P., 2012.) It would therefore be irresponsible to try and cover everything in the chapters that follow because, to paraphrase Frederick the Great, we would achieve nothing.

With this constraint in mind, I hope to provide a solid foundation of algorithms and business considerations that will allow the reader to walk away and, first of all, take on any machine learning tasks with complete confidence, and secondly, be able to help themselves in figuring out other algorithms and topics. Essentially, if this book significantly helps you to help yourself, then I would consider this a victory. Don't think of this book as a destination but rather, as a path to self-discovery.

The world of R can be as bewildering as the world of machine learning! There is seemingly an endless number of R packages with a plethora of blogs, websites, discussions, and papers of various quality and complexity from the community that supports R. This is a great reservoir of information and probably R's greatest strength, but I've always believed that an entity's greatest strength can also be its greatest weakness. R's vast community of knowledge can quickly overwhelm and/or sidetrack you and your efforts. Show me a problem and give me ten different R programmers and I'll show you ten different ways the code is written to solve the problem. As I've written each chapter, I've endeavored to capture the critical elements that can assist you in using R to understand, prepare, and model the data. I am no R programming expert by any stretch of the imagination, but again, I like to think that I can provide a solid foundation herein.

Another thing that lit a fire under me to write this book was an incident that happened in the hallways of a former employer a couple of years ago. My team had an IT contractor to support the management of our databases. As we were walking and chatting about big data and the like, he mentioned that he had bought a book about machine learning with R and another about machine learning with Python. He stated that he could do all the programming, but all of the statistics made absolutely no sense to him. I have always kept this conversation at the back of my mind throughout the writing process. It has been a very challenging task to balance the technical and theoretical with the practical. One could, and probably someone has, turned the theory of each chapter to its own book. I used a heuristic of sorts to aid me in deciding whether a formula or technical aspect was in the scope, which was would this help me or the readers in the discussions with team members and business leaders? If I felt it might help, I would strive to provide the necessary details.

I also made a conscious effort to keep the datasets used in the practical exercises large enough to be interesting but small enough to allow you to gain insight without becoming overwhelmed. This book is not about big data, but make no mistake about it, the methods and concepts that we will discuss can be scaled to big data.

In short, this book will appeal to a broad group of individuals, from IT experts seeking to understand and interpret machine learning algorithms to statistical gurus desiring to incorporate the power of R into their analysis. However, even those that are well-versed in both IT and statistics—experts if you will—should be able to pick up quite a few tips and tricks to assist them in their efforts.