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Machine Learning Examples for Data Science in R


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number



Overview/Description
R is a free software environment for statistical computing and graphics and has become an important tool in modern data science. In this course, you will learn the essential R machine learning methods that data scientists use in their everyday work.

Target Audience
Individuals with some statistics, programming, and machine learning experience who wish to learn machine learning methods in R used in data science in R

Prerequisites
None

Expected Duration (hours)
2.5

Lesson Objectives

Machine Learning Examples for Data Science in R

  • start the course
  • distinguish between supervised and unsupervised learning
  • perform classical multidimensional scaling using cmdscale in R
  • perform hierarchical cluster analysis in R
  • use the corclust function in the klaR package in R
  • perform k-means clustering on data in R
  • use the kselection package to select k for a k-means clustering in R
  • use the clusplot function to perform a cluster plot on a clara object in R
  • perform a fully C-Means clustering from the e1071 package in R
  • create a basic classification tree using rpart in R
  • create a basic regression tree using rpart in R
  • create a basic classification tree with the trees package in R
  • create a basic regression tree with the trees package in R
  • perform a K-Nearest Neighbor classification in R
  • use the randomforest package for classification in R
  • combine random forest ensembles into a single object in R
  • use random forests for unsupervised classification in R
  • use the clusplot function to perform a cluster plot on a pam cluster in R
  • build a na├»ve bayes classifier using the klaR package in R
  • use the lda function in R
  • use the qda function from the MASS package in R
  • perform a MDS using the mda package in R
  • use the SVM function from the e1071 library in R
  • perform a curve fit using the LOESS method in R
  • perform a PLS regression using the pls package in R
  • plot a smoothing spline from the splines packages in R
  • use the boosting function from the adabag package in R
  • use the bagging function from the adabag package in R
  • create a scatterplot matrix using the caret package in R
  • create an overlayed density plot using the caret package in R
  • create a 3D Scatterplot in R
  • provide a basic understanding of how to use common statistical methods for data analysis in R
  • Course Number:
    df_dsfd_a02_it_enus