Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Chapter 5. Association Rule Learning

In many practical applications data comes in the form of lists (ordered or unordered): grocery lists, playlists, visited locations or URLs, app logs, and so on. Sometimes those lists are generated as a byproduct of business processes, but they still contain potentially useful information and insights for process improvement. To extract some of that hidden knowledge, one can use a special kind of unsupervised learning algorithm—association rule mining. In this chapter, we are going to build an app that can analyze your shopping lists to find out your preferences in the form of rules such as "If you've bought oatmeal and cornflakes, you also want to buy milk." This can be used to create an adaptable user experience, for instance, contextual suggestions or reminders.

In this chapter, we will cover the following topics:

  • Association rules
  • Association measures
  • Association rule mining algorithms
  • Building an adaptable user experience