What do we mean by machine learning? Most computer programs today are handcrafted by humans. Software engineers carefully craft every rule that governs how software behaves and then translate it into computer code.

If you are reading this as an eBook, take a look at your screen right now. Everything that you see appears there because of some rule that a software engineer somewhere crafted. This approach has gotten us quite far, but that's not to say there are no limits to it. Sometimes, there might just be too many rules for humans to write. We might not be able to think of rules since they are too complex for even the smartest developers to come up with.

As a brief exercise, take a minute to come up with a list of rules that describe all dogs, but clearly distinguish dogs from all other animals. Fur? Well, cats have fur, too. What about a dog wearing a jacket? That is still a dog, just in a jacket. Researchers have spent years trying to craft these rules, but they've had very little success.

Humans don't seem to be able to perfectly tell why something is a dog, but they know a dog when they see a dog. As a species, we seem to detect specific, hard-to-describe patterns that, in aggregate, let us classify an animal as a dog. Machine learning attempts to do the same. Instead of handcrafting rules, we let a computer develop its own rules through pattern detection.

There are different ways this can work, and we're now going to look at three different types of learning: supervised, unsupervised, and reinforcement learning.