[MUSIC] I've been doing machine learning for
a long time, and it's a really exciting time to
be doing machine learning today, because we're really seeing
a lot of impact from it. But this is not how I
started machine learning. If you just talked to me a few years ago, the way I thought about machine
learning was quite different. You started with some data sets,
some data. And you fed it to some magical
machine learning algorithm and then I just showed you that my curve
was better than your curve and then I wrote the paper to
machine learning conference. And that's how I always thought
about machine learning. But this is not why I got
into the first place, why did I get into machine learning? Now when I was a kid I
read a lot of books. A lot of sci-fi. You know I really wanted to build robots
that were really intelligent that really thought about the world and reasoned about things, what I call
today intelligent applications. And the really cool and exciting thing
is that today we're seeing a lot of impact from intelligent applications,
they're using machine learning. In fact if you look at English
industry successful companies today. Companies that are called disruptive, they
take a market and completely change it. They're often differentiated
by intelligent applications, by intelligence that uses
machine learning at its core. So, for example, early days Amazon
really disrupted the retail market by bringing in product
recommendations into their website. We saw Google disrupting the advertising
market by really targeting advertising with machine learning to figure
out what people would click on. You saw Netflix,
the movie distribution company, really change how movies are seen. Now we don't go to a shop and
rent movies anymore. We go to the web and we stream data. Netflix really changed that. And at the core there was a recommender
system that helped me find the movies that I liked, the movies that are good for
me out of the many, many, many thousands of movies
they were serving. You see companies like Pandora,
where they're providing a music recommendation system
where I find music that I like. And I find streams that are good for
the morning when I'm sleepy or at night when I'm ready to go to bed and
I want to listen to different music. And they really find good music for us. And you see that in many places,
in many industries, you see Facebook connecting me with people
who I might want to be friends with. And you even see companies like Uber
disrupting the taxi industry by really optimizing how to connect drivers
with people in real time. So, in all these areas, machine learning
is one of the core technologies, the technology that makes that
company's product really special. So in this specialization, we're really
going to talk about all aspects of machine learning and get you ready to build
intelligent applications just like that. And we're gonna see a pipeline again,
and again, and again where we're gonna start from data
and really bring in a machine learning method that provides you with a new
kind of analysis of the data. And that analysis is gonna
give you intelligence. Intelligence like what product
am I likely to buy right now? And by taking this pipeline and working
through it in a wide range of settings and a wide range of applications, with a range
of algorithms, but really understanding how they connect together, you're
gonna be able to really build smart, intelligent applications of your own. [MUSIC]