Ch. 1 - Foundations of “tidy” Machine learning
Foundations of “tidy” machine learning
Nesting your data
Unnesting your data
Explore a nested cell
The map family of functions
Mapping your data
Expecting mapped output
Mapping many models
Tidy your models with broom
The three ways to tidy your model
Augmenting your data
Ch. 2 - Multiple Models with broom
Exploring coefficients across models
Tidy up the coefficients of your models
What can we learn about these 77 countries?
Evaluating the fit of many models
Glance at the fit of your models
Best and worst fitting models
Visually inspect the fit of many models
Augment the fitted values of each model
Explore your best and worst fitting models
Improve the fit of your models
Build better models
Predicting the future
Ch. 3 - Build, Tune & Evaluate Regression Models
Training, test and validation splits
The test-train split
Cross-validation dataframes
Measuring cross-validation performance
Build cross-validated models
Preparing for evaluation
Evaluate model performance
Building and tuning a random forest model
Build a random forest model
Evaluate a random forest model
Fine tune your model
The best performing parameter
Measuring the test performance
Build & evaluate the best model
Ch. 4 - Build, Tune & Evaluate Classification Models
Logistic regression models
Prepare train-test-validate parts
Build cross-validated models
Evaluating classification models
Predictions of a single model
Performance of a single model
Prepare for cross-validated performance
Calculate cross-validated performance
Random forest for classification
Tune random forest models
Random forest performance
Build final classification model
Measure final model performance
Wrap-up
About Michael Mallari
Michael is a hybrid thinker and doer—a byproduct of being a StrengthsFinder “Learner” over time. With nearly 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.
Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.
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