Machine Learning For Dummies. John Paul Mueller

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Introduction

      The term machine learning has all sorts of meanings attached to it today, especially after Hollywood (and other movie studios) have gotten into the picture. Films such as Ex Machina have tantalized the imaginations of moviegoers the world over and made machine learning into all sorts of things that it really isn’t. Of course, most of us have to live in the real world, where machine learning actually does perform an incredible array of tasks that have nothing to do with androids that can pass the Turing Test (fooling their makers into believing they’re human). Machine Learning For Dummies, 2^nd Edition gives you a view of machine learning in the real world and exposes you to the amazing feats you really can perform using this technology.

      Even though the tasks that you perform using machine learning may seem a bit mundane when compared to the movie version, by the time you finish this book, you realize that these mundane tasks have the power to impact the lives of everyone on the planet in nearly every aspect of their daily lives. In short, machine learning is an incredible technology — just not in the way that some people have imagined.

      This second edition of the book contains a significant number of changes, not the least of which is that it’s using pure Python code for the examples now upon request from our readers. You can still download R versions of every example, which is actually better than before when only some of the examples were available in R. In addition, the book contains new topics, including an entire chapter that discusses machine learning ethics.

About This Book

      Machines and humans learn in entirely different ways, which is why the first part of this book is essential to your understanding of machine learning. Machines perform routine tasks at incredible speeds, but still require humans to do the actual thinking.

      The second part of this book is about getting your system set up to use the various Python coding examples. The two setups work for desktop systems using Windows, Mac OS, or Linux, or mobile devices that have access to a Google Colab compatible browser.

      The third part of the book discusses math basics with regard to machine learning requirements. It prepares you to perform math tasks associated with algorithms used in machine learning to make either predictions or classifications from your data.

      The fourth part of the book helps you discover what to do about data that isn’t quite up to par. This part is also where you start learning about similarity and working with linear models. The most advanced chapter tells you how to work with ensembles of learners to perform tasks that might not otherwise be reasonable to complete.

      The fifth part of the book is about practical application of machine learning techniques. You see how to do things like classify images, work with opinions and sentiments, and recommend products and movies.

      The last part of the book contains helpful information to enhance your machine learning experience. This part of the book also contains a chapter specifically oriented toward ethical data use.

      To make absorbing the concepts easy, this book uses the following conventions: