If science fiction stories are to be believed, teaching machines to learn will inevitably lead 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.

Your impressions of machine learning may be very heavily influenced by these types of mass media depictions of artificial intelligence. And even though there may be a hint of truth to such tales; in reality, machine learning is focused on more practical applications. The task of teaching a computer to learn is tied more closely to a specific problem that would be a computer that can play games, ponder philosophy, or answer trivial questions. Machine learning is more like training an employee than raising a child.

Putting these stereotypes aside, by the end of this chapter, you will have gained a far more nuanced understanding of machine learning. You will be introduced to the fundamental concepts that define and differentiate the most commonly used machine learning approaches.

You will learn:

In a single sentence, you could say that machine learning provides a set of tools that use computers to transform data into actionable knowledge. To learn more about how the process works, read on.