1 Introduction

You interact with machine learning on a daily basis whether you recognize it or not. The advertisements you see online are of products you’re more likely to buy, based on the things you’ve previously bought or looked at. Faces in the photos you upload to social media platforms are automatically identified and tagged. Your car’s GPS predicts which routes will be busiest at certain times of day and re-plots your route to minimize journey length. Your email client progressively learns which emails you want and which ones you consider spam to make your inbox less cluttered, and your home personal assistant recognizes your voice and responds to your requests. From the small improvements to our daily lives such as this, to the big, society-changing ideas such as self-driving cars, robotic surgery, and the automated scanning for other Earth-like planets, machine learning has become an increasingly important part of modern life.

1.1  What is machine learning?

1.1.1  Artificial intelligence and machine learning

1.1.2  The difference between a model and an algorithm

1.2  Classes of machine learning algorithms

1.2.1  Differences between supervised, unsupervised, and semi-supervised learning

1.2.2  Classification, regression, dimension reduction, and clustering

1.2.3  A brief word on deep learning

1.3  Why use R for machine learning?

1.4  Which datasets will we use?

1.5  What will you learn in this book

1.6  Summary