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

Different types of linear regression algorithms

Based on the approach used and the number of input parameters, there are several types of linear regression algorithms to determine the real value of the target variable. In this chapter, you will learn how to implement the following algorithms using F#:

  • Simple Least Square Linear Regression

  • Multiple Linear Regression

  • Weighted Linear regression

  • Ridge Regression

  • Multivariate Multiple Linear Regression

These algorithms will be implemented using a robust industry standard open source .NET mathematics API called Math.NET. Math.NET has an F# friendly wrapper.