[MUSIC] Welcome to the first module
of the classification course. In this module we're going to
talk about linear classifiers, which are one of the most commonly
used classifiers out there. In particular, we're going to talk about
logistic regression, which is again one of the most commonly used linear classifiers
and one of the most useful ones. The concepts you learn here will actually
extend to lot of other classification methods beyond linear classifiers. You're going to learn
the fundamental concepts, but also the underlying algorithms that let you optimize the parameters of
this models to fit your training data. We'll start with motivating example,
the same one we used in the first course of the specialization,
a new kind of restaurant review system. Now there's a special day coming up for
me, and I want to go out for some great Japanese foods. And I want a really great restaurant for
it where I can eat amazing sushi. Seattle is a really great place
where we have a ton a different, highly rated Japanese restaurants. So how do I choose one? Well there are many aspects of restaurants
and many aspects that make them great. What I really care about is
amazing food and nice ambiance. If I look at a restaurant review, it will talk about different
aspects of the restaurant. So for example, a specific review might
have a sentence that says something like, the experience there was excellent. What does this tell me? Well if I look at this sentence, the sentence is positive about the
experience of going to this restaurant. Then there might be a next
sentence that says, my wife tried the ramen and
it was pretty forgettable. What does that mean? The ramen there, forgettable. Don't want to eat it. But I'm not going to restaurant for ramen, I don't care about
this sentence in the review. On the other hand, the same restaurant
might say, the sushi there was delicious, it was the best sushi in Seattle. Now that says a lot about the sushi, which is the thing I care about the most
with respect to that restaurant I choose. So every review has different aspects and
different sentences. I would like capture a sentiment of each
sentence so I can understand if it's good with respect to sushi which is
what I care the most about. So every sentence,
I'm going to feed it to a classifier. It's going to say, is this a positive
sentiment or is this a negative sentiment? And that's our task. [MUSIC]