Regression Math: Getting Started with Linear Regression

Math    |    Beginner
  • 14 videos | 1h 35m 54s
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 3 users Rating 4.7 of 3 users (3)
Linear Regression analysis is a simple yet powerful technique for quantifying cause and effect relationships. Use this course to get your head around linear regression as the process of fitting a straight line through a set of points. Learn how to define residuals and use the least square error. Define and measure the R-squared, implement regression analysis, visualize your data by computing a correlation matrix and plotting it in the form of a correlation heatmap, and use scatter plots as a prelude to performing the regression analysis. Finish by implementing the regression analysis first using functions that you write yourself and then using the scikit-learn python library. By the end of the course, you'll be able to identify the need for linear regression and implement it effectively.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Define linear regression and outline how regression is used in prediction
    Outline how residuals are used in regression
    Describe what's meant by the least square error
    Compute the best fit using partial derivatives
    Calculate r-squared of a regression model
    Summarize what comprises the normal equation
  • Visualize correlations of features
    Split train and test data and create computations
    Manually define a regression line
    Perform regression and view the predicted values
    View the r-squared and residuals in regression
    Implement regression models using libraries
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 12s
  • 11m 1s
  • Locked
    3.  Residuals in Regression
    7m 35s
  • Locked
    4.  The Computation of "The Best Fit"
    8m 34s
  • Locked
    5.  Partial Derivatives with Regression Models
    5m 23s
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    6.  Calculating R-squared
    8m 12s
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    7.  The Normal Equation
    9m 7s
  • Locked
    8.  Setting up Data and Viewing Correlations
    6m 12s
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    9.  Splitting Data for Regression
    8m 12s
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    10.  Defining the Slope and Intercept for Regression
    5m 8s
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    11.  Creating a Regression Line and Predictions
    10m 4s
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    12.  Viewing the Performance of a Regression Model
    5m 1s
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    13.  Performing Regression with Built-in Modules
    7m 6s
  • Locked
    14.  Course Summary
    2m 7s

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