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

Finding linear regression coefficients using F#

The following is an example problem that can be solved using linear regression.

For seven programs, the amount of disk I/O operations and processor times were measured and the results were captured in a list of tuples. Here is that list: (14,2), (16,5),(27,7) (42,9), (39, 10), (50,13), (83,20). The task for linear regression is to fit a model for these data points.

For this experiment, you will write the solution using F# from scratch, building each block one at a time.

  1. Create a new F# program script in LINQPad as shown and highlighted in the following image:

  2. Add the following variables to represent the data points:

  3. Add the following code to find the values needed to calculate b0 and b1:

  4. Once you do this, you will get the following output:

The following is the final output we receive:

Now in order to understand how good your linear regression model fits the data, we need to plot the actual data points as scatter plots and the regression line as a straight...