Unclassified recommendation
🔘 Book page: guidetodatamining.com
📌 For your review and personal analysis, copy and paste the address of the book page in your browser. The educational and research community will also thank you for evaluating this information (above) and for making rational and timely personal comments. Using appropriate language at all times. Special attention must be paid to the technical, ethical and labor implications of Artificial Intelligence (AI). It will affect us all. And it is everyone’s responsibility.
Summary
This book is a guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski (Zen Buddhist monk and computational linguist). This is a guide through data mining concepts in a programming point of view and provides many hands-on problems to practice and test. Its methodology is based on a practical and simple approach from the beginning.
Chapters
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
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.
Author
Ron Zacharski
🔘 Book page: guidetodatamining.com
📌 For your review and personal analysis, copy and paste the address of the book page in your browser. The educational and research community will also thank you for evaluating this information (above) and for making rational and timely personal comments. Using appropriate language at all times. Special attention must be paid to the technical, ethical and labor implications of Artificial Intelligence (AI). It will affect us all. And it is everyone’s responsibility.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Source code: zacharski/pg2dm-python
Book website: guidetodatamining.com
Liked this post? Follow this blog to get more.