Learn the basics of prediction using machine learning. This track covers predicting categorical and numeric responses via classification and regression, and discovering the hidden structure of datasets (unsupervised learning). Learn how to process data for modeling, how to train your models, how to visualize your models and assess their performance, and how to tune their parameters for better performance.
Supervised Machine Learning
in R
Supervised Machine Learning
Supervised learning methods are central to your journey in data science. Learn how to generate, explore, and evaluate machine learning models by leveraging the tools in the Tidyverse. You'll learn about multiple and logistic regression techniques, tree-based models, and support vector machines. Finally, you'll learn how to tune your model's parameters for better performance.
Machine Learning Scientist
with R
Machine Learning Scientist
Master the essential skills to land a job as a machine learning scientist! You'll augment your R programming skillset with the toolbox to perform supervised and unsupervised learning. You'll learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance. In the process, you'll get an introduction to Bayesian statistics, natural language processing, and Spark.
