Machine Learning For Dummies. John Paul Mueller

Чтение книги онлайн.

СКАЧАТЬ and press Enter,” you need to replace Your Name with your actual name.

Foolish Assumptions

This book is designed for novice and professional alike. You can either read this book from cover to cover or look up topics and treat the book as a reference guide. However, we’ve made some assumptions about your level of knowledge when we put the book together. You should already know how to use your device and work with the operating system that supports it. You also know how to perform tasks like downloading files and installing applications. You can interact with Internet well enough to locate the resources you need to work with the book. You know how to work with archives, such as the .zip file format. Finally, a basic knowledge of math is helpful.

Icons Used in This Book

      As you read this book, you see icons in the margins that indicate material of interest. This section briefly describes each icon.

      

The tips in this book are time-saving techniques or pointers to resources that you should try so that you can get the maximum benefit from machine learning.

      

You should avoid doing anything that's marked with a Warning icon. Otherwise, you might find that your application fails to work as expected, you get incorrect answers from seemingly bulletproof code, or (in the worst-case scenario) you lose data.

      

Whenever you see this icon, think advanced tip or technique. Skip these bits of information whenever you like.

      

This text usually contains an essential process or a bit of information that you must know to perform machine learning tasks successfully.

Beyond the Book

      If you want to email us, please do! Make sure you send your book-specific requests to: [email protected]. We want to ensure that your book experience is the best one possible. The blog entries at http://blog.johnmuellerbooks.com/ contain a wealth of additional information about this book. You can check out John’s website at http://www.johnmuellerbooks.com/. You can also access other cool materials:

       Cheat Sheet: A cheat sheet provides you with some special notes on things you can do with machine learning that not every other scientist knows. You can find the Cheat Sheet for this book at www.dummies.com. Type Machine Learning For Dummies in the Search box and click the Cheat Sheets option that appears.

       Errata: You can find errata by entering this book’s title in the Search box at www.dummies.com, which takes you to this book’s page. In addition to errata, check out the blog posts with answers to reader questions and demonstrations of useful book-related techniques at http://blog.johnmuellerbooks.com/.

       Companion files: The source code is available for download. All the book examples tell you precisely which example project to use. You can find these files at this book’s page at www.dummies.com. Just enter the book title in the Search box, click Books on the page that appears, click the book’s title, and scroll down the page to Downloads.We’ve also had trouble with the datasets used in the previous edition of this book. Sometimes the datasets change or might become unavailable. Given that you likely don’t want to download a large dataset unless you’re interested in that example, we’ve made the non-toy datasets (those available with a package) available at https://github.com/lmassaron/datasets. You don’t actually need to download them, though; the example code will perform that task for you automatically when you run it.

Where to Go from Here

      Most people will want to start this book from the beginning, because it contains a good deal of information about how the real world view of machine learning differs from what movies might tell you. However, if you already have a first grounding in the reality of machine learning, you can always skip to the next part of the book.

      Chapter 4 is where you want to go if you want to use a desktop setup, while Chapter 6 is helpful when you want to use a mobile device. Your preexisting setup may not work with the book’s examples because you might have different versions of the various products. It’s essential that you use the correct product versions to ensure success. Even if you choose to go with your own setup, consider reviewing Chapter 5 unless you’re an expert Python coder already.

      If you’re already an expert with Python and know how machine learning works, you could always skip to Chapter 7. Starting at Chapter 7 will help you get into the examples quickly so that you spend less time with basics and more time with intermediate machine learning tasks. You can always go back and review the previous materials as needed.

Part 1

      Introducing How Machines Learn

IN THIS PART …

      Discovering how AI really works and what it can do for you

      Considering what the term big data means

      Understanding the role of statistics in machine learning

      Defining where machine learning will take society in the future

Chapter 1

      Getting the Real Story about AI

      IN THIS CHAPTER

      

Seeing the dream; getting beyond the hype of artificial intelligence (AI)

      

Comparing AI to machine learning

      

Understanding the engineering portion of AI and machine learning

      

Delineating where engineering ends and art begins

СКАЧАТЬ