From the course: Deep Learning: Getting Started

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Linear regression

Linear regression

From the course: Deep Learning: Getting Started

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Linear regression

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- [Instructor] One of the basic statistical concepts that is used in machine learning is linear regression. It forms a key foundation for deep learning. Linear regression is a linear model that explains the relationship between two or more variables. We have a dependent variable y and an independent variable x. The model provides an equation to compute the value of y based on the value of x. To compute this, we need two constants called a, which is the slope, and an intercept, which is b. The formula for computing y is ax plus b. This provides a linear relationship between y and x. In reality, the relationship may not be perfectly linear, so there will be errors in predictions. Linear regression is used in regression problems to predict continuous variables. It can be applied for multiple independent variables like x1, x2, up to xn. In which case, there will be an equal n slope of values a1, a2, up to an. Let's look at an example for building a linear regression model. When we say we…

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