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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 Exercises13,230 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

    Free

    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
    Foundations of "tidy" machine learning
    50 xp
    Nesting your data
    100 xp
    Unnesting your data
    100 xp
    Explore a nested cell
    100 xp
    The map family of functions
    50 xp
    Mapping your data
    100 xp
    Expecting mapped output
    100 xp
    Mapping many models
    100 xp
    Tidy your models with broom
    50 xp
    The three ways to tidy your model
    50 xp
    Extracting model statistics tidily
    100 xp
    Augmenting your data
    100 xp
  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
    Exploring coefficients across models
    50 xp
    Tidy up the coefficients of your models
    100 xp
    What can we learn about these 77 countries?
    50 xp
    Evaluating the fit of many models
    50 xp
    Glance at the fit of your models
    100 xp
    Best and worst fitting models
    100 xp
    Visually inspect the fit of many models
    50 xp
    Augment the fitted values of each model
    100 xp
    Explore your best and worst fitting models
    100 xp
    Improve the fit of your models
    50 xp
    Build better models
    100 xp
    Predicting the future
    50 xp
  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
    Training, test and validation splits
    50 xp
    The test-train split
    100 xp
    Cross-validation data frames
    100 xp
    Measuring cross-validation performance
    50 xp
    Build cross-validated models
    100 xp
    Preparing for evaluation
    100 xp
    Evaluate model performance
    100 xp
    Building and tuning a random forest model
    50 xp
    Build a random forest model
    100 xp
    Evaluate a random forest model
    100 xp
    Fine tune your model
    100 xp
    The best performing parameter
    100 xp
    Measuring the test performance
    50 xp
    Build & evaluate the best model
    100 xp
  4. 4

    Build, Tune & Evaluate Classification Models

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

    Play Chapter Now
    Logistic regression models
    50 xp
    Prepare train-test-validate parts
    100 xp
    Build cross-validated models
    100 xp
    Evaluating classification models
    50 xp
    Predictions of a single model
    100 xp
    Performance of a single model
    100 xp
    Prepare for cross-validated performance
    100 xp
    Calculate cross-validated performance
    100 xp
    Random forest for classification
    50 xp
    Tune random forest models
    100 xp
    Random forest performance
    100 xp
    Build final classification model
    100 xp
    Measure final model performance
    100 xp
    Wrap-up
    50 xp

In the following tracks

Intermediate Tidyverse ToolboxMachine Learning Scientist with RSupervised Machine Learning in R

Datasets

GapminderAttrition

Collaborators

Chester Ismay
Sumedh Panchadhar
Eunkyung Park

Prerequisites

Modeling with Data in the Tidyverse
Dmitriy Gorenshteyn HeadshotDmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

Dmitriy is a Principal Data Scientist at Interos Inc. Previously, he worked in the Strategy & Innovation department at Memorial Sloan Kettering Cancer Center where he developed predictive models for programs aimed at improving patient care. Dmitriy completed his Doctorate in Quantitative & Computational Biology at Princeton University. His core teaching philosophy is centered on building intuition and understanding for the methods and tools available.
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