As we've learned, a data set is comprised of examples.

And each of those examples has common features

that a model can use to perform analysis and comparisons.

But what do we want a model to do with that data?

Ultimately, we want it to make some kind of a prediction.

And the prediction it makes is called a label.

Let's go back to the earlier example of a spam filter.

Each example, in this case an email, has features which,

in this case, might be things like the subject line, body, and sender.

In this case, the label is whether the message is spam or not spam.

A classifier is a type of algorithm or

model that makes a prediction about how a piece of data should be categorized.

You can think of a classifier like a function.

Data goes in, and then the classifier predicts the correct category for

that data.

It does this by using an existing data set that has examples where the labels

are known.

So for a spam filter, you would train the classifier with a data set

where lots of emails are already labeled as spam or not spam.

And then when a new email comes in, it can try to assign a label.

There's one more thing I want to mention before we carry on.

Cleaning and organizing data in different ways can often produce different results.

In the case of the emails, you might find that the raw data from the email doesn't

make useful features because further heuristics need to be applied.

For a spam filter classifier, you might create features that counts

the number of spammy phrases from a dictionary.

Like free offer or click here, or a feature that identifies

an attachment as a photo or an executable program that might be a virus.

There's an old saying in computing called garbage in, garbage out.

It means that if you provide the computer with bad information, or

if you give your machine learning model a data set that's inaccurate or

not representative of the whole truth.

Then you're going to get a bad result.

And that's it.

As you can imagine, there are many more definitions and terms in machine learning.

But those are the big ones we'll need in order to continue with our exercise

in the next videos.

Where we're going to write our own classifier in Python

using a library called scikit-learn.