About this course
Gain insights to help improve your machine learning models and statistical analyses.
Prediction, causation, and statistical inference
1m 27sLady tasting tea
4m 48sWhy causation matters in a business setting
1m 27sWhat is a causal model?
2m 13sSkepticism about data: Truman 1948 Election Poll
3m 7sSkepticism about results: Is that really the best predictor?
3m 47sSkepticism about causes: Is X really causing Y?
2m 59sWhat is a strong correlation?
6m 53sPearson on correlation and causation
5m 1sCorrelation and regression
5m 6sChallenge: What is causing what?
1m 53sSolution: What is causing what?
7m 9sUsing probability to measure uncertainty
8m 23sp-value review
1m 28sHypothesis testing checklist
3m 59sTaleb on normality, mediocristan, and extremistan
2m 39sChallenge: Evaluate significant finding
1m 50sSolution: Evaluate significant finding
5m 34sWhat are induction and deduction?
4m 21sHume on induction
3m 35sPopper on induction and falsification
4m 32sTaleb on induction
4m 24sCounterfactuals: Pearl on induction and causality
2m 24sData mining vs. data dredging
5m 49sTrain/Test: What can go wrong?
4m 57sA/B testing during the evaluation phase
2m 45sThe Two Cultures
5m 6sExplain vs. predict
5m 4sComparing CRISP-DM and the scientific method
4m 35sApplying the two methods at work
4m 11sReview
1m 4s