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Nonlinear Modeling in R with GAMs

930/month
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Self paced
English
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DatacampCourses from Datacamp
Certificate awarded
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

This course is not affiliated with any university.

Price

This course costs very less.

Why should you choose this course?

Description

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. By the end of this course, you'll have a toolbox for solving many data science problems.

Syllabus

Introduction to Generalized Additive Models
Spatial GAMs and Interactions
Interpreting and Visualizing GAMs
Logistic GAMs for Classification

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