Supervised Machine Learning: Regression

IBM develops regression from linear models into polynomial fits, cross-validation and regularization. Ridge, LASSO and elastic net receive explicit coverage before the final project. The course is a focused option for learners predicting continuous values rather than surveying every machine-learning task.

What you will learn

• Fit and evaluate linear or polynomial regression models.
• Use cross-validation to compare modeling choices.
• Explain the effect of regression regularization.

Who this suits

Intermediate learners; detailed prerequisites are not listed in the published course facts. Preview the required Python and statistics background.

Before you enroll

Inspect the project’s error metrics and data-splitting rules. A lower training error can reflect overfitting, so compare models on data that did not determine their parameters.

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Put it into practice

Compare ordinary and regularized regression on the same dataset. Plot training and validation errors and explain which model you would choose for a new observation.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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