Machine Learning - Stanford - Coursera
1.0.0
1.0.0
  • Acknowledgements
  • Introduction
  • Linear Algebra Review
  • Types of Machine Learning
  • Supervised Learning
    • Linear Regression
      • Linear Regression in One Variable
        • Cost Function
        • Gradient Descent
      • Multivariate Linear Regression
        • Cost Function
        • Gradient Descent
        • Feature Scaling
        • Mean Normalization
        • Choosing the Learning Rate α
    • Polynomial Regression
      • Normal Equation
      • Gradient Descent vs. Normal Equation
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  1. Supervised Learning
  2. Linear Regression

Linear Regression in One Variable

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Last updated 5 years ago

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This is a supervised learning algorithm where we estimate the value of a dependent target variable using a linear combination of operations on an independent variable.

It is also called Univariate Linear Regression.

There is one input and one output.

Since it is a form of supervised learning, the end result is already known.

The general hypothesis function is of the form:

hθ(x)=θ0+θ1xh_θ(x) = θ_0 + θ_1xhθ​(x)=θ0​+θ1​x