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Welcome to the regression course of the machine learning specialization.
So, regression is one of the most widely used statistics and
machine learning tools for deriving intelligence from data.
And the methods allow you to do anything from predicting the price of stocks
to understanding gene regulatory networks.
In the first course of this specialization,
we talked about regression at a very high level.
And implemented some of the methods.
But in this course, we're gonna go into a lot more detail.
And really look inside the hood at what are the underlying models and
algorithms that allow us to do this regression task.
This course is a part of the machine learning specialization
that's designed to be taken in a certain sequence.
So although you can take this course as a stand-alone course,
in order to get the experience that we intended,
we strongly encourage you to take the entire sequence of this specialization.
So, in particular we're assuming that you've seen the content from
the foundations course, which provided a very high level overview of
all the content that we're gonna see in this specialization.
And gave you some hands on practice with the different methods.
In this course we're focusing on regression but
as a part of this course we're gonna teach you a bunch of very general concepts that
are useful in many different aspects of machine learning.
So although we're gonna be describing things in the context of models and
algorithms for regression,
some of the concepts are gonna carry through in the rest of the specialization.
So subsequent courses that we're gonna see include a course on classification,
clustering and retrieval, recommender systems.
And then all of this content will include with a capstone project involving deep
learning that's going to pull in ideas from other aspects of this specialization.
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