第一部分: While we believe that the world is moving forward with better versions coming out, a lot of

developers still enjoy using Python 2.x. A lot of operating systems have Python 2.x built into them. This

course is focused on machine learning in Python as opposed to Python itself. It also helps in maintaining

compatibility with libraries that haven’t been ported to Python 3.x. Hence the code in the book is

oriented towards Python 2.x. In that spirit, we have tried to keep all the code as agnostic as possible to

the Python versions.

第二部分: The entirety of this course’s content leverages openly available data and code,including open

source Python libraries and frameworks. While each chapter’s example code is accompanied by a

README file documenting all the libraries required to run the code provided in that chapter’s

accompanying scripts, the content of these files is collated here for your convenience. It is recommended

that some libraries required for earlier chapters be available when working with code from any later

chapter. These requirements are identified using bold text. Particularly, it is important to set up the first

chapter’s required libraries for any content later in the book.

第三部分: The execution of the code examples provided in this book requires an installation of Python

2.7 or higher versions on macOS, Linux, or Microsoft Windows.

The examples throughout the book will make frequent use of Python’s essential libraries, such as SciPy,

NumPy, Scikit-learn, and StatsModels, and to a minor extent, matplotlib and pandas, for scientific and

statistical computing. We will also make use of an out-of-core cloud computing application called H2O.

This book is highly dependent on Jupyter and its Notebooks powered by the Python kernel. We will use

its most recent version, 4.1, for this book. The first chapter will provide you with all the step-by-step

instructions and some useful tips to set up your Python environment, these core libraries, and all the

necessary tools.