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?