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Supervised Learning in R: Classification
In this course you will learn the basics of machine learning for classification.
Supervised Learning in R: Regression
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Unsupervised Learning in R
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning in the Tidyverse
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Intermediate Regression in R
Learn to perform linear and logistic regression with multiple explanatory variables.
Cluster Analysis in R
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Tree-Based Models in R
In this course, you'll learn how to use tree-based models and ensembles for regression and classification.
Support Vector Machines in R
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Fundamentals of Bayesian Data Analysis in R
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Topic Modeling in R
Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.
Hyperparameter Tuning in R
Learn how to tune your model's hyperparameters to get the best predictive results.
Bayesian Regression Modeling with rstanarm
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.