Udemy – R tidymodels part 1: Introduction

Whether you’re a data analyst, aspiring data scientist, or a tidyverse user seeking to enhance your modeling skills, this course introduces you to tidymodels, a powerful and consistent framework for statistical modeling and machine learning in R.

This course gives you a solid foundation in predictive modeling. We begin with the fundamentals of regression modeling and guide you through the complete modeling workflow using tidymodels:

• Understand what modeling is — and how it’s different from just analyzing data.
• Grasp the principles of statistical learning and machine learning.
• Build, validate, and interpret linear regression models.
• Discover the power of penalized regression (ridge, lasso, elastic net).
• Learn the concept of the bias-variance trade-off and how regularization helps.
• Apply consistent, tidy workflows for:
  • preprocessing with recipes
  • modeling with parsnip
  • resampling with rsample
  • evaluation with yardstick
  • tuning with tune

  You’ll walk away not just knowing how to use the tools, but understanding the modeling process itself!

  • Anyone who is interested in data science
  • Anyone who is interested in statistics
  • Anyone who is interested in building predictive models using machine learning
  • Anyone who is interested in writing efficient R code
  • Anyone whose job, research or hobby is related to building predictive models
  • Aspiring data scientists, statisticians or machine learning engineers
  • Anyone who deals with data modeling and would like to get familiar with modern R approach for modeling
  • Students building predictive models
  • Data scientist who mainly use python in their work, and would like to extend their skills into R domain