Let's talk a little bit more about a specific example
of supervised learning. We might want to ask the question, can we
write an equation that takes as input a bunch of
information. For example, height, weight, birth year or position about baseball
players and predicts their lifetime number of home runs. To
do this, we want to write an algorithm that does the
following. Takes in the data points for which we have all
these input attributes, and the player's lifetime number of home runs.
Then builds the most accurate equation to predict lifetime number of
home runs, using these input variables. We can then use this equation
to predict the lifetime number of home runs for players for
whom we did not initially have number of home runs. But we
do have all the other data. So, their height and weight
and birth year and position maybe. One way that we might be
able to solve this problem is using linear regression and one
basic implementation on machine learning is
to perform linear regression by using
gradient descent. What is this? How does this work? Lets learn about
gradient descent by working through an
example that utilizes our baseball data set.