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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.
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