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Nina B. Zumel

Tagged “talks”

  1. Squeezing the Most Utility from your AI/ML Models
  2. Tobit Regression with Stan
  3. Advanced Data Preparation for Supervised Machine Learning
  4. Preparing Messy Data For Supervised Learning (Python)
  5. Practical Data Science with R
  6. Myths of Data Science: Things You Should and Should Not Believe
  7. Improving Prediction using Nested Models and Simulated Out-of-Sample Data
  8. Statistically Validate Models with R
  9. Validating Models in R
  10. An Introduction to Differential Privacy as Applied to Machine Learning
  11. Prepping Data for Analysis Using R

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