Practical Data Science: Reducing High Dimensional Data in R

Practical Data Science: Reducing High Dimensional Data in R

In this R course, we'll see how PCA can reduce a 5000+ variable data set into 10 variables and barely lose accuracy!

What Will I 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
Includes:
  • 2.5 hours on-demand video
  • 5 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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