#### 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.
F# for Machine Learning Essentials
Credits
Foreword
Acknowledgments
www.PacktPub.com
Preface
Free Chapter
Introduction to Machine Learning
Information Retrieval
Collaborative Filtering
Sentiment Analysis
Index

## Putting it together with Math.NET and FsPlot

In this example, you will see how Math.NET and FsPlot can be used together to generate the linear regression coefficients and plot the result. For this example, we will use a known relation between Relative Humidity (RH) and Dew point temperature. The relationship between relative humidity and dew point temperature is given by the following two formulas:

Here, t and td are the temperatures in degrees Celsius.

td is dew point, which is a measure of atmospheric moisture. It is the temperature to which the air must be cooled in order to reach saturation (assuming the air pressure and the moisture content are constant).

Let's say the dew point is 10 degrees Celsius, then we can see how linear regression can be used to find a relationship between the temperature and RH.

The following code snippet generates a list of 50 random temperatures and then uses the formula to find the RH. It then feeds this data into a linear regression system to find the best...