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

Multivariate multiple linear regression

When you want to predict multiple target values for the same set of predictor variables, you need to use multivariate multiple linear regression. Multivariate linear regression takes an array of a set of predictors and an associated list of outcomes for each of this predictor set of values.

In this example, we will use Accord.NET to find the relationships between several data:

  1. Get Accord Statistics via NuGet by giving the following command in PM console:

    PM> Install-Package Accord.Statistics -Version 2.15.0
  2. Once you install this package, the following code finds coefficients of the multivariate linear regression for sample data: