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


After reading this chapter, you will be able to apply some of the techniques to identify any anomaly in data, and you will have a general understanding of how and where anomaly detection algorithms can be useful. All code is available at

Different classification algorithms

The following algorithms will be discussed in this chapter:

  • Statistical anomaly detection

  • Nearest neighbor based anomaly detection

  • Density estimation based anomaly detection

Some cool things you will do

With the techniques learned from this chapter, you will be able to spot lies. You will also have a deeper understanding of how fraudulent behaviors on credit cards are found.

The different types of anomalies

Anomalies can be classified into any of the following categories:

  • Point anomalies

  • Contextual anomalies

  • Collective anomalies

We will go through each one of them in detail:

  • Point anomalies: If an individual data instance can be considered as anomalous with respect...