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

Dealing with categorical data in collective anomalies

As an another illustrative example, consider a sequence of actions occurring in a computer, as shown below:

: : : http-web, buffer-overflow, http-web, http-web, smtp-mail, ftp, http-web, ssh, smtp-
mail, http-web, ssh, buffer-overflow, ftp, http-web, ftp, smtp-mail,http-web : : :

The highlighted sequence of events (buffer-overflow, ssh, ftp) corresponds to a typical, web-based attack by a remote machine followed by the copying of data from the host computer to a remote destination via ftp. It should be noted that this collection of events is an anomaly, but the individual events are not anomalies when they occur in other locations in the sequence.

These types of categorical data can be transformed into numeric data by assigning a particular number for each command. If the following mapping is applied to transform categorical data to numeric data:


Numeric Representation