Practical Data Science: Reducing High Dimensional Data in R

Rating 4.35 out of 5 (144 ratings in Udemy)

What you'll learn

Understand various ways of reducing wide data sets
Understand Principal Component Analysis (PCA)
Control, tune and measure the effects of PCA
Use GBM modeling to measure the effectiveness of PCA
Reducing dimensionality with classic GBM & GLMNET Variable Selection
Use ensembling techniques to find the most stable variables

Description

In this R course, we'll see how PCA can reduce a 5000+ variable data set down to 10 …

Duration 2 Hours 58 Minutes

Paid

Self paced

All Levels

English (US)

1792

Rating 4.35 out of 5 (144 ratings in Udemy)

Go to the Course
We have partnered with providers to bring you collection of courses, When you buy through links on our site, we may earn an affiliate commission from provider.