Machine Learning for beginner

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II. Linear Regression with One Variable (Week 1)

Model Representation

Recall that in regression problems, we are taking input variables and trying to map the output onto a continuous expected result function.
Linear regression with one variable is also known as "univariate linear regression."
Univariate linear regression is used when you want to predict a single output value from a single input value. We're doing supervised learning here, so that means we already have an idea what the input/output cause and effect should be.
 

The Hypothesis Function

Our hypothesis function has the general form: 
hθ(x)=θ0+θ1x

 

We give to hθ values for θ0 and θ1 to get our output 'y.' In other words, we are trying to create a function called hθ that is able to reliably map our input data (the x's) to our output data (the y's).
 
Example:
x (input) y (output)
0 4
1 7
2 7
3 8

Now we can make a random guess about our hθ function: θ0=2 and θ1=2. The hypothesis function becomes hθ(x)=2+2x.

So for input of 1 to our hypothesis, y will be 4. This is off by 3.
 
 

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