Must know stats for ML practitioners:

  1. Know what a p-value is and its limitations in decisions.
  2. Linear regression and its assumptions.
  3. When to use different statistical distributions.
  4. How an effect size impacts results/decisions.
  5. Mean, variance for Normal, Uniform, Poisson.
  6. Sampling techniques and common designs (e.g. A/B).
  7. Bayes’ theorem (applied calculations).
  8. Confidence intervals measurement and interpretation.
  9. Logistic regression and ROC curves.
  10. Resampling (Cross validation + bootstrapping).
  11. Dimensionality reduction.
  12. Tree-based models (particularly how to prune).
  13. Ridge and Lasso for regression.

🤓Time to brush up my statistics…