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.
Overview
Syllabus
- 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.
- Predict Future Sales of Fast-Food Menu Items
- Unsupervised Learning in R
- This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
- Machine Learning with caret in R
- This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
- Modeling with tidymodels in R
- Learn to streamline your machine learning workflows with tidymodels.
- Machine Learning with Tree-Based Models in R
- Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Taught by
Zachary Deane-Mayer, Max Kuhn, Hank Roark, Brett Lantz, John Mount, Nina Zumel, David Svancer, and Sandro Raabe
