Welcome to the fourth and final course
of this specialization,
just one course away from finishing
this whole specialization and
have you learnt a lot about tensor flow.
In this course, you learned about sequence models,
what are sequence models?
So what we're going to be
focusing on is one part of sequence models,
which is really time-series.
Sequence models where it's a case of if you can
imagine a series of data that changes over time.
It might be the closing prices
for stock on the stock exchange,
or it could be weather.
It could be how sunny it is in California on a given day,
or how rainy it is in Seattle
on a given day, that type of thing.
So if you just imagine how
an item of data changes
over time and how it's measured over time.
So basically almost anything like a spreadsheet,
if I've a spreadsheet where I have one day per
row and two columns say one for the California weather,
one for the Seattle weather to document how
better my life is to yours in Seattle than in California,
and then we would have a new network help us model that.
Exactly. So we're going to start by
creating a synthetic sequence of data,
so that we can start looking at what
the common attributes that you see in data series are.
So for example, whether data can be seasonal.
It's sunnier in June than it is in January
or it's wetter in November than it is in October,
something along those lines.
So you have that seasonality of data.
You can also, in some cases,
have trends of data,
like whether it probably doesn't really trend although we
could argue that it strangely enough
idea with climate change,
but like a stock data may trend upwards
over time or downwards over some other times,
and then of course the random factor
that makes it hard to predict is noise.
So you can have like seasonal data,
you can have trends in your data,
but then you can have noise on that data as well.
So that the average temperature of a Tuesday
in June and California might be 85 degrees,
but it might be 85.5 degrees,
it might be 84.5 degrees.
So you get that noise in the data.
So we want to start
looking at various methods that can be used
statistically in the Machine Learning to help us
predict data given seasonality trend and noise.
Then in this course, at the end of this course,
one of the most cool applications
is to use these ideas to model sunspot.
Sunspot activity. Yeah. So sunspot activity
is really interesting
because the sun has
an 11-year cycle although
some astronomers tell me it's a 22-year cycle,
there's actually two 11-year cycles
like nestled beside each other.
Will we resolve this in this course?
That remains to be seen.
You're going to have to study
all the way through and then we'll see,
but the idea then is that you
do get that nice seasonality,
and we have data measuring
back about 250 years worth of sunspot activity.
So that's on a monthly basis counting the number
of sunspots that had been spotted by astronomers.
So we do definitely see
that 11 year cycle or maybe the 22-year cycle,
and there's a lot of noise in there,
the seasonality and that stuff.
So it's fun to build
something to protect sunspot activity.
In fact, sunspot activity is very important to NASA and
other space agencies because
it affects satellite operations.
So in this course,
you'll start by learning about sequence models,
a time series data,
first practicing these skills and building
these models on artificial Data,
and then at the end of this course,
you get to take all these ideas and apply them to
the exciting problem of molding sunspot activity.
So let's get started.
Please go on to the next video.