Chapter 1, Premodel Workflow, walks you through the preparatory step of preparing a dataset for modeling and shows how scikit-learn can help to ameliorate the burden of preprocessing.

Chapter 2, Working with Linear Models, discusses how many problems can be viewed as linear models upon the appropriate application of a transformation, and therefore walks you through what may be the most used class of models.

Chapter 3, Building Models with Distance Metrics, encompasses a large number of topics that largely work by measuring the similarity between the data points. Because similarity and distance are often synonymous, clustering can often be used as long as a distance function can be defined.

Chapter 4, Classifying Data with scikit-learn, focuses on the various methods within scikit-learn that are used to determine a data point as some member between 1 and N classes.

Chapter 5, Postmodel Workflow, teaches us how we can take a basic model produced from one of the recipes and tune it so that we can achieve better results than we could with the basic model.

Here are the contents of the requirements.txt file that will get the environment set up. This will allow you to follow along with the code in the book.

I've also included a conda requirements file; this method may be easier for less-experienced Python developers:

dateutil==2.1
ipython==2.2.0
ipython-notebook==2.1.0
jinja2==2.7.3
markupsafe==0.18
matplotlib==1.3.1
numpy==1.8.1
patsy==0.3.0
pandas==0.14.1
pip==1.5.6
pydot==1.0.28
pyparsing==1.5.6
pytz==2014.4
pyzmq==14.3.1
scikit-learn==0.15.0
scipy==0.14.0
setuptools==3.6
six==1.7.3
ssl_match_hostname==3.4.0.2
tornado==3.2.2

This book can help budding analysts who are familiar with Python to take the next step into machine learning with scikit-learn. It is assumed that you are familiar with Python, but beyond that we'll touch on many of the important aspects of scikit-learn. On top of that, we'll discuss enough theory to help you ask the next question after you've figured out the nuances of scikit-learn.

This book contains the following sections:

In this book, you will find a number of styles of text 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, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "From within IPython, run datasets.*?, which will list everything available within the datasets module."

Any command-line input or output is written as follows:

>>> transformed = dl.fit_transform(iris_data[::2])
>>> transformed[:5]

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Notice the peak around 0. This will naturally lead to the zero coefficients in lasso regression."

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