Machine Learning Basics | Chapter 6: Supervised Learning – Linear Regression by Prakhar Chauhan | Learn Smarter
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Chapter 6: Supervised Learning – Linear Regression

Supervised Learning involves training models on labeled datasets where input features correspond to known outputs. Linear Regression is a fundamental supervised learning algorithm utilized to model relationships between dependent and independent variables. This chapter covers the implementation of linear regression in Python, along with the evaluation of model performance using metrics like Mean Squared Error and R² Score.

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Sections

  • 6

    Supervised Learning – Linear Regression

    This section covers the fundamentals of supervised learning and introduces linear regression as a method to model the relationship between variables.

    Learning Practice

  • 6.1

    What Is Supervised Learning?

    Supervised learning involves training a model on a labeled dataset, where the model learns to predict outputs based on provided inputs.

    Learning Practice

  • 6.2

    Introduction To Linear Regression

    Linear Regression is a foundational supervised learning algorithm used to model the relationship between dependent and independent variables through a straight line.

    Learning Practice

  • 6.3

    Dataset Example

    This section introduces a small dataset correlating years of experience with salary, demonstrating how to create and view the dataset in Python.

    Learning Practice

  • 6.4

    Visualizing The Data

    This section discusses the importance of visualizing data before training a linear regression model, focusing on creating scatter plots to understand the relationship between the dependent and independent variables.

    Learning Practice

  • 6.5

    Training The Linear Regression Model

    This section discusses the process of training a linear regression model using the scikit-learn library in Python, detailing the setup of features and target variables.

    Learning Practice

  • 6.6

    Interpreting The Model

    This section explains how to interpret the coefficients of a linear regression model, specifically focusing on the slope and intercept.

    Learning Practice

  • 6.7

    Making Predictions

    This section discusses how to use the linear regression model to make predictions based on input features.

    Learning Practice

  • 6.8

    Plotting The Regression Line

    This section explains how to visualize the regression line for a simple linear regression model using a scatter plot and the fitted line.

    Learning Practice

  • 6.9

    Evaluating Model Performance

    This section explains how to evaluate the performance of linear regression models using Mean Squared Error (MSE) and the R² Score.

    Learning Practice

  • 6.9.1

    Summary

    Learning

References

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Class Notes

Memorization

What we have learnt

  • Supervised learning involve...
  • Linear regression models th...
  • Model performance can be ev...

Final Test

Revision Tests