The main purpose of Machine Learning For Dummies is to help you understand what machine learning can and can’t do for you today and what it might do for you in the future. You don’t have to be a computer scientist to use this book, even though it does contain many coding examples. In fact, you can come from any discipline that heavily emphasizes math because that’s how this book focuses on machine learning. Instead of dealing with abstractions, you see the concrete results of using specific algorithms to interact with big data in particular ways to obtain a certain, useful result. The emphasis is on useful because machine learning has the power to perform a wide array of tasks in a manner never seen before.
Part of the emphasis of this book is on using the right tools. This book uses both Python and R to perform various tasks. These two languages have special features that make them particularly useful in a machine learning setting. For example, Python provides access to a huge array of libraries that let you do just about anything you can imagine and more than a few you can’t. Likewise, R provides an ease of use that few languages can match. Machine Learning For Dummies helps you understand that both languages have their role to play and gives examples of when one language works a bit better than the other to achieve the goals you have in mind.
You also discover some interesting techniques in this book. The most important is that you don’t just see the algorithms used to perform tasks; you also get an explanation of how the algorithms work. Unlike many other books, Machine Learning For Dummies enables you to fully understand what you’re doing, but without requiring you to have a PhD in math. After you read this book, you finally have a basis on which to build your knowledge and go even further in using machine learning to perform tasks in your specific field.