Linear Regression with single variable

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Linear Regression Algorithm

From Andrew Ng’s Machine learning course

The above diagram conveniently gives a brief overview of the Linear Regression Algorithm. We feed our learning algorithm with some data set(training set), which then outputs a function called Hypothesis.
Hypothesis approximates a target function for mapping inputs to outputs.
For linear regression in one variable, our hypothesis function is of the form —

h(x) = θ0 + θ1x
where θ0 and θ1 are the parameters.
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