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What you need for this book
The execution of the code examples provided in this book requires an installation
of Python 3.6.0 or newer on macOS, Linux, or Microsoft Windows. We will make
frequent use of Python's essential libraries for scientic computing throughout this
book, including SciPy, NumPy, scikit-learn, Matplotlib, and pandas.
The rst chapter will provide you with instructions and useful tips to set up your
Python environment and these core libraries. We will add additional libraries to
our repertoire; moreover, installation instructions are provided in the respective
chapters: the NLTK library for natural language processing (Chapter 8, Applying
Machine Learning to Sentiment Analysis), the Flask web framework (Chapter 9,
Embedding a Machine Learning Model into a Web Application), the Seaborn library
for statistical data visualization (Chapter 10, Predicting Continuous Target Variables
with Regression Analysis), and TensorFlow for efcient neural network training on
graphical processing units (Chapters 13 to 16).
Who this book is for
If you want to nd out how to use Python to start answering critical questions of
your data, pick up Python Machine Learning, Second Edition—whether you want to
start from scratch or extend your data science knowledge, this is an essential and
unmissable resource.
Conventions
In this book, you will nd a number of text styles that distinguish between different
kinds of information. Here are some examples of these styles and an explanation of
their meaning.
Code words in text, database table names, folder names, lenames, le extensions,
pathnames, dummy URLs, user input, and Twitter handles are shown as follows:
"Using the
out_file=None setting, we directly assigned the dot data to a dot_data
variable, instead of writing an intermediate tree.dot le to disk."