Overview
Context of the Nonlinear Modeling with Generalized Additive Models (GAMs) in R course at Data Camp
Generalized Additive Models are a powerful tool for both prediction and inference. More flexible than linear models, and more understandable than black-box methods, GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data and data science problems. In this course, 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.
Programme Structure
Chapters
- Interpreting and Visualizing GAMs
- Spatial GAMs and Interactions
- Logistic GAMs for Classification
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
Language
Delivered
Disciplines
Information Technology (IT) Computer Sciences Data Science & Big Data View 746 other Short Courses in Computer Sciences in United StatesAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
PREREQUISITES
- Introduction to Regression in R
Tuition Fee
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days.
Basic Access: Free; Premium (for individuals): $12.42 per month billed annually; Teams: $25 per month billed annually; Enterprise: Contact sales for pricing