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
caret R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
In the first chapter of this course, you'll fit regression models with
train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
In this chapter, you'll fit classification models with
train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
In this chapter, you will use the
train() function to tweak model parameters through cross-validation and grid search.
In this chapter, you will practice using
train() to preprocess data before fitting models, improving your ability to making accurate predictions.
In the final chapter of this course, you'll learn how to use
resamples() to compare multiple models and select (or ensemble) the best one(s).
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