Linear Models Course
This course equips you to master linear models from initial data preparation right through to deployment. Statistics professionals will learn to construct, diagnose, and interpret robust regression models, manage bias and uncertainty, and convert statistical coefficients into straightforward, actionable business insights that drive real decisions.
4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Gain a practical pathway to constructing dependable predictive models through linear regression techniques. Delve into data exploration, feature engineering, and fitting basic, multiple, and regularised regressions using Python or R. Master model validation, assumption testing, outlier and missing data management, plus effective communication of results, uncertainty, and business implications for informed decisions.
Elevify advantages
Develop skills
- Develop strong linear models using efficient OLS, regularisation techniques, and smart feature engineering.
- Identify and fix model problems like residuals, multicollinearity, outliers, and overall robustness.
- Assess model performance with metrics such as R-squared, RMSE, AIC/BIC, cross-validation, and confidence intervals.
- Present findings clearly by explaining coefficients, uncertainty levels, and their business relevance.
- Adopt a production-ready approach covering model monitoring, data drift detection, retraining, and ethical practices.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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