Chapter 1, Getting started with data mining, introduces the technologies we will be using, along with implementing two basic algorithms to get started.

Chapter 2, Classifying with scikit-learn, covers classification, a key form of data mining. You’ll also learn about some structures for making your data mining experimentation easier to perform..

Chapter 3, Predicting Sports Winners with Decisions Trees, introduces two new algorithms, Decision Trees and Random Forests, and uses it to predict sports winners by creating useful features..

Chapter 4, Recommending Movies using Affinity Analysis, looks at the problem of recommending products based on past experience, and introduces the Apriori algorithm.

Chapter 5, Features and scikit-learn Transformers, introduces more types of features you can create, and how to work with different datasets.

Chapter 6, Social Media Insight using Naive Bayes, uses the Naïve Bayes algorithm to automatically parse text-based information from the social media website Twitter.

Chapter 7, Follow Recommendations Using Graph Mining, applies cluster analysis and network analysis to find good people to follow on social media.

Chapter 8, Beating CAPTCHAs with Neural Networks, looks at extracting information from images, and then training neural networks to find words and letters in those images.

Chapter 9, Authorship attribution, looks at determining who wrote a given documents, by extracting text-based features and using Support Vector Machines.

Chapter 10, Clustering news articles, uses the k-means clustering algorithm to group together news articles based on their content.

Chapter 11,Object Detection in Images using Deep Neural Networks, determines what type of object is being shown in an image, by applying deep neural networks.

Chapter 12, Working with Big Data, looks at workflows for applying algorithms to big data and how to get insight from it.

Appendix, Next step, goes through each chapter, giving hints on where to go next for a deeper understanding of the concepts introduced.

It should come as no surprise that you’ll need a computer, or access to one, to complete the book. The computer should be reasonably modern, but it doesn’t need to be overpowered. Any modern processor (from about 2010 onwards) and 4 gigabytes of RAM will suffice, and you can probably run almost all of the code on a slower system too.

The exception here is with the final two chapters. In these chapters, I step through using Amazon’s web services (AWS) for running the code. This will probably cost you some money, but the advantage is less system setup than running the code locally. If you don’t want to pay for those services, the tools used can all be set-up on a local computer, but you will definitely need a modern system to run it. A processor built in at least 2012, and more than 4 GB of RAM are necessary.

I recommend the Ubuntu operating system, but the code should work well on Windows, Macs, or any other Linux variant. You may need to consult the documentation for your system to get some things installed though.

In this book, I use pip for installing code, which is a command line tool for installing Python libraries. Another option is to use Anaconda, which can be found online here: http://continuum.io/downloads

I also have tested all code using Python 3. Most of the code examples work on Python 2 with no changes. If you run into any problems, and can’t get around it, send an email and we can offer a solution.

This book is for programmers that want to get started in data mining in an application-focused manner.

If you haven’t programmed before, I strongly recommend that you learn at least the basics before you get started. This book doesn’t introduce programming, nor does it give too much time to explaining the actual implementation (in-code) of how to type out the instructions. That said, once you go through the basics, you should be able to come back to this book fairly quickly – there is no need to be an expert programmer first!

I highly recommend that you have some Python programming experience. If you don’t, feel free to jump in, but you might want to take a look at some Python code first, possibly focused on tutorials using the IPython notebook. Writing programs in the IPython notebook works a little differently than other methods, such as writing a Java program in a fully-fledged IDE.

In this book, you will find 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, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code read the link and assign it to the to the dataset_filename function."

A block of code is set as follows:

import numpy as np 
dataset_filename = "affinity_dataset.txt" 
X = np.loadtxt(dataset_filename)

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

$ conda install scikit-learn

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."

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