Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular <code>caret</code> R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.
train()and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
train()and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
train()function to tweak model parameters through cross-validation and grid search.
train()to preprocess data before fitting models, improving your ability to making accurate predictions.
resamples()to compare multiple models and select (or ensemble) the best one(s).
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Lloyds Banking Group
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Harvard Business School
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