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

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And each of those examples
has common features

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that a model can use to perform
analysis and comparisons.

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

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

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And the prediction it
makes is called a label.

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

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

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

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

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A classifier is a type of algorithm or

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

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You can think of a classifier
like a function.

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

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that data.

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

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are known.

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

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

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

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

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

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

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make useful features because further
heuristics need to be applied.

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

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the number of spammy
phrases from a dictionary.

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

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

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

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

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

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not representative of the whole truth.

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Then you're going to get a bad result.

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And that's it.

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

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

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in the next videos.

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

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using a library called scikit-learn.