[MUSIC] 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. [MUSIC]