About the bookBefore you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don’t get me wrong, the information in those books is extremely important. However, if you are a programmer interested in learning a bit about data mining you might be interested in a beginner’s hands-on guide as a first step. That’s what this book provides. This guide follows a learn-by-doing approach. Instead of passively reading the book, I encourage you to work through the exercises and experiment with the Python code I provide. I hope you will be actively involved in trying out and programming data mining techniques. The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques.

Table of ContentsChapter 1 Introduction

Finding out what data mining is and what problems it solves. What will you be able to do when you finish this book.
Chapter 2: Get Started with Recommendation Systems

Introduction to social filtering. Basic distance measures including Manhattan distance, Euclidean distance, and Minkowski distance. Pearson Correlation Coefficient. Implementing a basic algorithm in Python.
Chapter 3: Implicit ratings and item-based filtering

A discussion of the types of user ratings we can use. Users can explicitly give ratings (thumbs up, thumbs down, 5 stars, or whatever) or they can rate products implicitly–if they buy an mp3 from Amazon, we can view that purchase as a ‘like’ rating.
Chapter 4: Classification

In previous chapters we used people’s ratings of products to make recommendations. Now we turn to using attributes of the products themselves to make recommendations. This approach is used by Pandora among others.
Chapter 5: Further Explorations in Classification

A discussion on how to evaluate classifiers including 10-fold cross-validation, leave-one-out, and the Kappa statistic. The k Nearest Neighbor algorithm is also introduced.
Chapter 6: Naïve Bayes

An exploration of Naïve Bayes classification methods. Dealing with numerical data using probability density functions.
Chapter 7: Naïve Bayes and unstructured text

This chapter explores how we can use Naïve Bayes to classify unstructured text. Can we classify twitter posts about a movie as to whether the post was a positive review or a negative one?
Chapter 8: Clustering

Clustering – both hierarchical and kmeans clustering.


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