New Book: A Programmer Guide to Data Mining – Free Download
New book "A Programmer Guide to Data Mining" - a guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. Free download of all chapters.
A guide to practical data mining, collective intelligence, and building recommendation systems.
[Gregory Piatetsky: I have read a couple of chapters of this book, and it combines a very entertaining, visual style of presentation with clear explanations and do-it-yourself examples. Enjoy!]
This guide follows a learn-by-doing approach. You are encouraged to work through the exercises and experiment with the Python code provided.
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. This book is available for download for free under a Creative Commons license.
The link for chapter titles below take you to chapter pages, which have table of content, PDF, and links to any sample Python code and data.
- Chapter 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
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: Naive Bayes
An exploration of Naive Bayes classification methods. Dealing with numerical data using probability density functions.
- Chapter 7: Naive Bayes and unstructured text
This chapter explores how we can use Naive 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 (forthcoming)
For full information visit guidetodatamining.com/
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