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F# for Machine Learning Essentials

You're reading from  F# for Machine Learning Essentials

Product type Book
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Pages 194 pages
Edition 1st Edition
Languages
Author (1):
Sudipta Mukherjee Sudipta Mukherjee
Profile icon Sudipta Mukherjee

Table of Contents (16) Chapters

F# for Machine Learning Essentials
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction to Machine Learning Linear Regression Classification Techniques Information Retrieval Collaborative Filtering Sentiment Analysis Anomaly Detection Index

Working with real movie review data (Movie Lens)


You can download the Movie Lens 100K dataset (for collaborative filtering) from http://files.grouplens.org/datasets/movielens/ml-100k/.

This dataset has movie ratings given by 943 users for 1,682 movies. These ratings are stored in the u.data file at http://files.grouplens.org/datasets/movielens/ml-100k/u.data. The full u data set has 100,000 ratings by 943 users on 1,682 items.

Each user has rated at least 20 movies. The users and items are numbered consecutively from 1. The data is randomly ordered. This is a tab separated list of:

user id | item id | rating | timestamp.

The time stamps are in Unix seconds since 1/1/1970 UTC.

The following C# program translates this data to an F# array so that this data can be fed to the collaborative filtering algorithms implemented earlier in the chapter:

https://gist.github.com/sudipto80/606418978f4a86fe93aa

Once you generate this array, you can then plug this into the algorithms described earlier.

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