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Welcome to the regression course of
the machine learning specialization.

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So, regression is one of the most
widely used statistics and

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machine learning tools for
deriving intelligence from data.

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And the methods allow you to do anything
from predicting the price of stocks

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to understanding gene regulatory networks.

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In the first course of
this specialization,

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we talked about regression
at a very high level.

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And implemented some of the methods.

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But in this course,
we're gonna go into a lot more detail.

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And really look inside the hood at
what are the underlying models and

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algorithms that allow us
to do this regression task.

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This course is a part of
the machine learning specialization

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that's designed to be taken
in a certain sequence.

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So although you can take this
course as a stand-alone course,

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in order to get the experience
that we intended,

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we strongly encourage you to take the
entire sequence of this specialization.

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So, in particular we're assuming
that you've seen the content from

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the foundations course, which provided
a very high level overview of

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all the content that we're gonna
see in this specialization.

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And gave you some hands on practice
with the different methods.

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In this course we're
focusing on regression but

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as a part of this course we're gonna teach
you a bunch of very general concepts that

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are useful in many different
aspects of machine learning.

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So although we're gonna be describing
things in the context of models and

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algorithms for regression,

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some of the concepts are gonna carry
through in the rest of the specialization.

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So subsequent courses that we're gonna
see include a course on classification,

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clustering and retrieval,
recommender systems.

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And then all of this content will include
with a capstone project involving deep

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learning that's going to pull in ideas
from other aspects of this specialization.

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