Course
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Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Track
Generate, explore, evaluate, and tune the parameters of different supervised machine learning models.
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Make progress on the go with our mobile courses and daily 5-minute coding challenges.
Course
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Course
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn to perform linear and logistic regression with multiple explanatory variables.
Project
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Course
Learn to streamline your machine learning workflows with tidymodels.
Learn to streamline your machine learning workflows with tidymodels.
Course
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Supervised Machine Learning in R
Track
Complete
Data Scientist @ codecentric
Yes, this track is suitable for beginners. It is designed to help students gain domain-specific expertise in supervised machine learning and will teach the tools in the Tidyverse, regression techniques, tree-based models, and support vector machines. Hyperparameter tuning and model parameter tuning will also be covered.
This track uses the R programming language.
This track is particularly beneficial to data scientists, machine learning engineers, and researchers, though any job that requires knowledge of supervised machine learning will benefit from this track.
This track will provide students with a comprehensive overview of supervised machine learning methods and processes. With this knowledge, students will be better prepared to work as a data scientist in their field of choice.
This track typically takes 25 hours to complete.
A skill track focuses on teaching individual concepts and skills, while a career track focuses on helping students to develop the skills and competencies they need for a particular career path.
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