Tidymodels in R: Building tidy machine learning models
By External Provider
About this course
This guided project aims to empower data professionals to build tidy machine-learning models in R. In this 2-hour project-based course, you will be working in the context of a real-world scenario as part of a data-science team tasked with reducing hospital readmissions for a leading healthcare organization. Through hands-on practice, you’ll learn to preprocess clinical data and train and evaluate machine learning models. By the end of this learning experience, you'll have created a comprehensive machine-learning pipeline tailored to predict hospital readmissions. To succeed, you'll need a good understanding of R programming language, including data manipulation and visualization using tidyverse packages and some knowledge of machine learning concepts. No prior experience with Tidymodels is required, making it accessible to anyone interested in leveraging data science for healthcare analytics. Join us on this transformative journey and become equipped to make a meaningful impact on patient care outcomes through data-driven insights.
What you will learn
- Import, explore, and prepare the data for modeling using summary tables, data visualizations, and data splitting using tidymodels
- Build and tune predictive models, including classification models with tidymodels
- Evaluate model performance using appropriate metrics and techniques, and select the best model