Читать книгу Machine Learning For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 66

IN THIS CHAPTER

Determining which Python distribution to use for machine learning

Performing a Linux, Mac OS X, and Windows installation

Obtaining the datasets and example code

Before you can do too much with Python or use it to solve machine learning problems, you need a workable installation. In addition, you need access to the datasets and code used for this book. This chapter tells you how to perform the required Python setups and downloads. Downloading the sample code (found at this book’s page at www.dummies.com) and installing it on your system is the best way to get a good learning experience from the book. This chapter helps you get your system set up so that you can easily follow the examples in the remainder of the book.

Using the downloadable source code doesn’t prevent you from typing the examples on your own, following them using a debugger, expanding them, or working with the code in all sorts of ways. The downloadable source code is there to help you get a good start with your machine learning and Python learning experience. After you see how the code works when it’s correctly typed and configured, you can try to create the examples on your own. If you make a mistake, you can compare what you’ve typed with the downloadable source code and discover precisely where the error exists. You can find the downloadable source for this chapter in the ML4D2E; 04; Sample.ipynb and ML4D2E; 04; Dataset Load.ipynb files. (The Introduction tells you where to download the source code for this book.)

The downloadable source also provides access to the examples written as R variants. Although the Python code appears in the ML4D2E (for Machine Learning For Dummies, 2nd Edition) folder of the downloadable source, the R code appears in the ML4D2ER (for Machine Learning For Dummies, 2nd Edition, R code) folder. The R examples don't always precisely follow the Python examples because of the differences in the two languages, but the R examples are heavily annotated so that you can follow along. Using either language will allow you to reach the desired result.