Book Image

F# for Machine Learning Essentials

By : Sudipta Mukherjee
Book Image

F# for Machine Learning Essentials

By: Sudipta Mukherjee

Overview of this book

The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
Table of Contents (16 chapters)
F# for Machine Learning Essentials
About the Author
About the Reviewers

Working with real movie review data (Movie Lens)

You can download the Movie Lens 100K dataset (for collaborative filtering) from

This dataset has movie ratings given by 943 users for 1,682 movies. These ratings are stored in the file at 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:

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