Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Start Course for Free
5 Hours15 Videos52 Exercises9,952 Learners
4300 XP

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

This course will teach you to leverage the tools in the "tidyverse" to generate, explore, and evaluate machine learning models. Using a combination of tidyr and purrr packages, you will build a foundation for how to work with complex model objects in a "tidy" way. You will also learn how to leverage the broom package to explore your resulting models. You will then be introduced to the tools in the test-train-validate workflow, which will empower you evaluate the performance of both classification and regression models as well as provide the necessary information to optimize model performance via hyperparameter tuning.

  1. 1

    Foundations of "tidy" Machine learning

    This chapter will introduce you to the backbone of machine learning in the tidyverse, the List Column Workflow (LCW). The LCW will empower you to work with many models in one dataframe.
    This chapter will also introduce you to the fundamentals of the broom package for exploring your models.
    Play Chapter Now
  2. 2

    Multiple Models with broom

    This chapter leverages the List Column Workflow to build and explore the attributes of 77 models. You will use the tools from the broom package to gain a multidimensional understanding of all of these models.
    Play Chapter Now
  3. 3

    Build, Tune & Evaluate Regression Models

    In this chapter you will learn how to use the List Column Workflow to build, tune and evaluate regression models. You will have the chance to work with two types of models: linear models and random forest models.
    Play Chapter Now
  4. 4

    Build, Tune & Evaluate Classification Models

    In this chapter you will shift gears to build, tune and evaluate classification models.
    Play Chapter Now
In the following tracks
Intermediate Tidyverse ToolboxMachine Learning ScientistSupervised Machine Learning
Sumedh PanchadharChester IsmayEunkyung Park
Dmitriy Gorenshteyn Headshot

Dmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center
Dmitriy is a Lead Data Scientist in the Strategy & Innovation department at Memorial Sloan Kettering Cancer Center. At MSK he develops predictive models for programs aimed at improving patient care. Prior to this role, Dmitriy completed his Doctorate in Quantitative & Computational Biology at Princeton University. With a passion for teaching and for R, he regularly holds cross-departmental R training sessions within MSK. His core teaching philosophy is centered on building intuition and understanding for the methods and tools available.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA