Applied Machine Learning in R

After completing the course, you will…

  • Understand the essential concepts related to machine learning
  • Perform model cross-validation to assess model stability on independent data sets
  • Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
  • Perform logistic regression and discriminant analysis
  • Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees
  • Use neural networks to make predictions
  • Use principal components analysis to detect patterns in variables
  • Conduct cluster analysis to group observations into homogeneous classes

Who this course is for:

  • Data analysts
  • Data scientists
  • Researchers
  • Students
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