Machine Learning and AI Foundations : Prediction, Causation and Statistical Inference
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In this video we are going to see the basics of Machine Learning and AI . These are the fundamentals of Machine Learning and AI and essential trainings. we will see more and more in upcoming videos.
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//Chapters and time splits
00:00:00-00:01:27 Prediction, causation and statistical inference
00:01:27-00:06:15 Lady tasting tea
00:06:16-00:07:42 Why causation matters in a business setting
00:07:43-00:09:55 What is a causal model?
00:09:56-00:13:02 Skepticism about data: Truman 1948 Election Poll
00:13:03-00:16:49 Skepticism about results: Is that really the best predictor?
00:16:50-00:19:48 Skepticism about causes: Is X really causing Y?
00:19:49-00:26:41 What is a strong correlation?
00:26:42-00:31:42 Pearson on correlation and causation
00:31:43-00:36:48 Correlation and regression
00:36:49-00:38:41 Challenge: What is causing what?
00:38:42-00:45:50 Solution: What is causing what?
00:45:51-00:54:13 Using probability to measure uncertainty
00:54:14-00:55:41 p-value review
00:55:42-00:59:40 Hypothesis testing checklist
00:59:41-01:02:19 Taleb on normality, mediocristan and extremistan
01:02:20-01:04:09 Challenge: Evaluate significant finding
01:04:10-01:09:43 Solution: Evaluate significant finding
01:09:44-01:14:04 What are induction and deduction
01:14:05-01:17:39 Hume on induction
01:17:40-01:22:11 Popper on induction and falsification
01:22:12-01:26:35 Taleb on induction
01:26:36-01:28:59 Counterfactuals: Pearl on induction and causality
01:29:00-01:34:48 Data mining vs data dredging
01:34:49-01:39:45 Train/Test: What can go wrong?
01:39:46-01:42:30 A/B testing during the evaluation phase
01:42:31-01:47:36 The two Cultures
01:47:37-01:52:40 Explain vs predict
01:52:41-01:57:15 Comparing CRISP-DM and the scientific method
01:57:16-02:01:26 Applying the two methods at work
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