Machine Learning with R at PAW 2020

The Machine Learning with R: A Hands-on Introduction workshop at Predictive Analytics World 2020 will use your own computer for the hands-on part. I emailed instructions to all registered participants on Monday, May 18th. If you have not received the email, check your spam folder. If you registered after that date, I’ll send the files as soon as the PAW2020 people send me new registration information. Anyone having problems installing the software should contact me, Bob Muenchen, at muenchen.bob@gmail.com. See you online soon!

Here’s the workshop outline:

  1. INTRODUCTION
  2. BASIC CONCEPTS
  3. TRAINING & TESTING DATA
  4. RECIPES
  5. DATA PRE-PROCESSING STEPS
  6. TREE MODELS (we will cover 5 of these 10 algorithms, chosen via a survey)
  7. RANDOM FORESTS
  8. GRADIENT BOOSTING MACHINES
  9. NEURAL NETWORKS
  10. K NEAREST NEIGHBORS
  11. SUPPORT VECTOR MACHINES
  12. NAIVE BAYES
  13. LOGISTIC REGRESSION
  14. DISCRIMINANT ANALYSIS
  15. LINEAR REGRESSION ANALYSIS
  16. EVALUATING MODEL EFFECTIVENESS
  17. CROSS-VALIDATION METHODS
  18. TUNING GRIDS
  19. MODEL TUNING
  20. CLASS IMBALANCE ISSUES
  21. INTERPRETING BLACK-BOX MODELS
  22. CHOOSING A MODEL
  23. CONCLUSION