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Course
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Model Validation and Hyperparameter Tuning in R
Advance your data science skills by mastering model validation and hyperparameter tuning in R. Through practical demos, you'll learn to validate models, prevent overfitting, and optimize hyperparameters for peak predictive performance.
What you'll learn
Accurate model validation and optimized hyperparameters are essential for delivering reliable machine learning results in high-stakes environments. In this course, Model Validation and Hyperparameter Tuning in R, you’ll gain the ability to improve model performance by implementing robust validation techniques and fine-tuning hyperparameters. First, you’ll explore how to assess model reliability through train-test splits and k-fold cross-validation using R packages like caret and tidymodels. Next, you’ll discover how to identify overfitting and underfitting using performance metrics such as RMSE and AUC. Finally, you’ll learn how to apply grid search, random search, and Bayesian optimization to systematically tune hyperparameters and maximize predictive accuracy. When you’re finished with this course, you’ll have the skills and knowledge of advanced model evaluation and tuning techniques needed to confidently deliver high-performing machine learning models using R.
Table of contents
About the author
Dr. Daniel “Brian” Letort is a 22+ year veteran of Information Technology. During a 21-year tenure at Northrop Grumman, Brian held various roles across software engineering, systems engineering, Chief Applications Architect, Chief Data Scientist, and Chief Enterprise Architect. Brian held the NG Fellow title for six years and Technical Fellow title for four years prior. In 2022, Brian joined Digital Realty as the Chief Architect - Product and Artificial Intelligence. Aside from working at Digital Realty, Brian has 12+ years experience in teaching Data Science and Computer Science classes as an adjunct professor. Brian has authored two books and holds two patents.
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