In the earlier courses in this specialization, you got an introduction to
machine learning and deep learning with a heavy focus on computer vision problems.
You learned about neural networks and
how they can match patterns to perform classifications.
And then how you can give them new data and
have them predict what they might be seeing.
You learned how to make that a little smarter for
images using convolutions to identify the features in the images and
classify based on those instead of just matching on raw pixels.
This helped you classify images for
more real world style ones instead of using a very controlled environment.
In this course we're going to go back to building models but
we'll focused on text and how you can build classifier is based on text models.
We'll start by looking at sentiment in text, and
learn how to build models that understand text that are trained on labeled text, and
then can then classify new text based on what they've seen.
When we were dealing with images, it was relatively easy for
us to feed them into a neural network, as the pixel values were already numbers.
And the network could learn parameters of functions that could be used to fit
classes to labels.
But what happens with text?
How can we do that with sentences and words?