Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
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5 Hours15 Videos52 Exercises8,523 Learners
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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.
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  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.
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  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.
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  4. 4

    Build, Tune & Evaluate Classification Models

    In this chapter you will shift gears to build, tune and evaluate classification models.
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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.
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