• Tried to fit a straight line

Training set

• We need a training set to do regression or classification

• These are the notations that we are going to use

  • m = Number of training examples

  • x's = "input" variable /features . (size in feet above)

  • y's = "output" variable/ target feature (Price above)

  • (x,y) = single training example

  • x(i), y(i) = ith training example

Hypothesis function

How do we represent h?

lets say we fit this in linear function -> h(x) = Q0 + Q1x

so we are predicting that y is some linear function of x

Above is called linear regression with one variable