If science fiction stories are to be believed, the invention of artificial intelligence inevitably leads to apocalyptic wars between machines and their makers. In the early stages, computers are taught to play simple games of tic-tac-toe and chess. Later, machines are given control of traffic lights and communications, followed by military drones and missiles. The machines' evolution takes an ominous turn once the computers become sentient and learn how to teach themselves. Having no more need for human programmers, humankind is then "deleted."
Thankfully, at the time of this writing, machines still require user input.
Though your impressions of machine learning may be colored by these mass media depictions, today's algorithms are too application-specific to pose any danger of becoming self-aware. The goal of today's machine learning is not to create an artificial brain, but rather to assist us in making sense of the world's massive data stores.
Putting popular misconceptions aside, by the end of this chapter, you will gain a more nuanced understanding of machine learning. You also will be introduced to the fundamental concepts that define and differentiate the most commonly used machine learning approaches.
You will learn:
The origins and practical applications of machine learning
How computers turn data into knowledge and action
How to match a machine learning algorithm to your data
The field of machine learning provides a set of algorithms that transform data into actionable knowledge. Keep reading to see how easy it is to use R to start applying machine learning to real-world problems.