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Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Welcome to the tidyverse! In this course, you will continue on your journey to learn the tidyverse and apply your knowledge to machine learning concepts.
This course is ideal if you’re looking to integrate R's Tidyverse tools into your machine learning workflows.
Throughout this course, you will focus on leveraging the tidyverse tools in R to build, explore, and evaluate machine learning models efficiently.
The course begins by introducing the List Column Workflow (LCW), a method for managing multiple models within a single dataframe. It also covers using the broom package to tidy up and explore model outputs, making the complex results more interpretable.
Work through practical exercises including building and evaluating regression along with classification models. Explore techniques for tuning hyperparameters to optimize model performance.
You will use packages like tidyr and purrr to handle complex data manipulations and model evaluations, ensuring a tidy and systematic approach to machine learning.
Explore real-world examples through multiple case studies, such as using the gapminder dataset to predict life expectancy with linear models.
By the end of the course, you will have a strong foundation in applying Tidyverse principles to machine learning, enabling them to build, tune, and evaluate models efficiently in a tidy and reproducible manner.
Welcome to the tidyverse! In this course, you will continue on your journey to learn the tidyverse and apply your knowledge to machine learning concepts.
This course is ideal if you’re looking to integrate R's Tidyverse tools into your machine learning workflows.
Evaluating machine learning models
Throughout this course, you will focus on leveraging the tidyverse tools in R to build, explore, and evaluate machine learning models efficiently.
The course begins by introducing the List Column Workflow (LCW), a method for managing multiple models within a single dataframe. It also covers using the broom package to tidy up and explore model outputs, making the complex results more interpretable.
Utilizing tidyr and purrr
Work through practical exercises including building and evaluating regression along with classification models. Explore techniques for tuning hyperparameters to optimize model performance.
You will use packages like tidyr and purrr to handle complex data manipulations and model evaluations, ensuring a tidy and systematic approach to machine learning.
Gain real-world application
Explore real-world examples through multiple case studies, such as using the gapminder dataset to predict life expectancy with linear models.
By the end of the course, you will have a strong foundation in applying Tidyverse principles to machine learning, enabling them to build, tune, and evaluate models efficiently in a tidy and reproducible manner.
