This course has been a broad overview of machine learning.

We looked at a lot of big ideas and we even exported some code.

Let's do a quick review.

Machine Learning is a new approach to artificial intelligence.

With an emphasis on statistical analysis, and the ability for the computer to write

its own set of rules, rather than humans writing all of the conditional logic.

There are two major categories of Machine Learning, Supervised Learning and

Unsupervised Learning.

Supervised learning is when a machine intelligence predicts a category or

a quantity using models of classification and regression respectively.

Unsupervised learning is when a computer analyzes unlabeled data and

attempts to recognize patterns.

The most common unsupervised models use

clustering to group similar things together.

When handling data, remember that an example is a single element in a dataset.

And a feature is one characteristic of an example.

These basic models can lead to more complex and emerging behaviors in specific

domains like chatbots, image recognition, speech recognition and more.

More specialized applications like this can be served by higher level machine

learning platforms including AWS and IBM Watson.

If you didn't understand everything, don't worry.

That's normal.

I encourage you to watch this course again, explore the teacher notes,

and look at the documentation for scikit-learn.

You may also want to try exploring more problems using scikit-learn.

See if you can apply classification to another data set.

Or try something more advanced like regression or clustering.

With more practice and

repetition, your understanding will grow and you'll be ready for more.