Machine Learning #3
Linear Regression with One variable | Model Representation
Univariate Linear Regression
In supervised learning, we have a dataset also called a training set. the job is to learn from the data how to predict a continuous-valued output.
: Case 0.1:
Given a table of house prices and the respective sizes as our dataset
If we want to predict the price of a house given its size:
m = number of training examples (dataset, as per rows)
x’s = “input” variable/features (size of house)
y’s = “output” variable/”target” variable (price of house)
(x, y) —> Single datapoint (row)
(x^i, y^i) —> Specific training example ( ith training example), where i is the index.
How Supervised Learning Works
Start by feeding your dataset to the learning algorithm, it’s the job of the learning algorithm to then output a function which by convention is usually denoted h (meaning hypothesis). the job of the hypothesis is to take an input and predict a valued output.
size of house —> h —> Estimated price
h maps from x —> y
x = size of house
y = estimated price
Photo from Andrew NG ‘s ML class.
Woow!! I was afraid of machine learning at first. But, It seems I am darlington is making me fall in love.