Nonlinear Modeling with Generalized Additive Models (GAMs) in R Data Camp

Overview

Context

Generalized Additive Models are a powerful tool for both prediction and inference. More flexible than linear models, and more understandable than black-box methods, 

In this Nonlinear Modeling with Generalized Additive Models (GAMs) in R course offered by Data Camp, you'll learn how GAMs work and how to construct them with the popular mgcv package. You'll learn how to interpret, explain and visualize your model results, and how to diagnose and fix model problems. 

You'll work with data sets that will show you how to apply GAMs to a variety of situations: automobile performance data for building mixed linear and nonlinear models, soil pollution data for building geospatial models, and consumer purchasing data for classification and prediction. By the end of this course, you'll have a toolbox for solving many data science problems.