All the Statistics You Need for AI Engineering - In 4 Hrs!

AI Engineering Accelerator Program: http://dsaccelerator.com Timestamps: 00:00 Introduction & Course Promise 03:03 Instructor Background & Experience 04:44 Course Overview & Modules *Module 1: Introduction to Statistics* 08:41 Module 1 Overview 09:50 The $1M Netflix Prize Story 13:09 What is Statistics? 18:39 Descriptive vs. Inferential Statistics 20:03 Types of Data: Numerical vs. Categorical 22:02 Subtypes of Data: Discrete, Continuous, Nominal, & Ordinal 23:56 Module 1 Recap *Module 2: Essential Descriptive Statistics* 24:53 Module 2 Overview 26:03 The 1854 London Cholera Outbreak & Jon Snow 30:16 Central Tendency: Mean, Median, & Mode 34:04 Variability & Spread: Variance, Standard Deviation, & IQR 39:08 Visualizing Trends with Histograms 41:33 Understanding Box-and-Whisker Plots 44:25 Module 2 Recap *Module 3: Probability* 45:24 Module 3 Overview 46:49 Abraham Wald & WWII Survivorship Bias 52:18 Probability Basics: Events & Outcomes 54:05 Conditional Probability 58:18 Bayes' Theorem & Intuition 1:03:44 Frequency Counting Approach 1:06:12 The Monty Hall Problem 1:11:22 Central Limit Theorem (CLT) 1:19:10 CLT Interactive Visual Simulation 1:22:10 History & Origins of Probability Theory 1:24:48 Probability Interview Questions 1:28:35 Module 3 Recap *Module 4: Probability Distributions* 1:30:07 Module 4 Overview 1:31:10 The Normal Distribution & Bell Curve 1:34:41 The 68-95-99.7 Rule 1:38:52 Pareto Distribution (80/20 Rule) 1:40:57 Bernoulli Distribution 1:42:51 Binomial Distribution 1:45:29 Poisson Distribution 1:48:47 Probability Distribution Interview Questions 1:51:21 Module 4 Recap *Module 5: Sampling & Inferential Statistics* 1:52:17 Module 5 Overview 1:53:19 The 1936 Literary Digest Polling Failure 1:55:48 Population vs. Sample & Sampling Bias 1:58:34 Random vs. Stratified Sampling 2:00:35 Bootstrapping Concept 2:02:00 Confidence Intervals Explained 2:04:08 Sample Size Determination & Power Analysis 2:08:26 Sampling Interview Questions 2:10:24 Hands-on Python Lab: Bootstrapping in Google Colab 2:14:46 Module 5 Recap *Module 6: Hypothesis Testing* 2:15:32 Module 6 Overview 2:17:57 The Lady Tasting Tea Story (RA Fisher) 2:20:18 Null vs. Alternative Hypothesis 2:22:24 Understanding the P-Value 2:26:19 Statistical Power & Type I / Type II Errors 2:31:49 Fundamentals of A/B Testing 2:32:21 Common Tests: T-Test, ANOVA, & Chi-Square 2:39:11 Multi-Armed Bandits 2:41:53 Hypothesis Testing Interview Questions 2:43:10 Module 6 Recap *Module 7: Causal Inference* 2:44:32 Module 7 Overview 2:47:09 UC Berkeley Admissions & Simpson's Paradox 2:53:05 Hormone Replacement Therapy Medical Disaster 2:55:18 DAGs, Confounders, Mediators, & Colliders 2:59:43 Quasi-Experiments: RD, Diff-in-Diff, & Instrument Variables 3:07:53 P-Hacking & Publication Bias 3:12:16 Advanced Experimental Designs 3:14:27 Causal Analysis Protocol 3:17:17 Causal Inference Interview Questions 3:22:44 Hands-on Python Lab: Simpson's Paradox 3:29:20 Module 7 Recap *Module 8: Bayesian vs. Frequentist Statistics* 3:30:48 Module 8 Overview 3:31:44 The Enigma Machine & WWII 3:34:02 Frequentist School of Thought 3:35:30 Bayesian School of Thought 3:37:46 Alan Turing's Bayesian Approach to Enigma 3:39:55 Summary: Frequentist vs. Bayesian Comparison 3:41:53 Course Wrap-Up & Final Remarks