Lec-4.2 Inferential Statistics (Arif Butt @ Data Science)
0:00 Introduction
03:18 Learning Agenda
04:09 Section 1: (Overview of Probability for Machine Learning)
04:31 Overview of Probability
06:33 Joint Probability
09:02 Combinatorics
10:35 Practical Implementation in Python
14:00 The Law of Large Numbers
18:37 Conditional Probability
20:52 Bayes’ Theorem
25:08 Applications of Bayes' Theorem
25:31 Naive Bayes' Classifier
32:46 How Probability Relates with Statistics
37:18 Probability Distributions for Discrete Random Variables
38:25 Probability Distributions for Continuous Random Variables
40:45 Normal Distribution
43:31 Standard Normal or Z-Distribution
45:24 Practical Implementation of Distributions in Python
49:10 Z-Score vs P-Value
51:15 Central Limit Theorem
56:28 Section 2: (Hypothesis Testing)
58:55 How to formulate a Hypothesis?
01:05:30 Types of Hypothesis Tests.
01:07:50 Related Terminologies (Rejection region, significance level, test scores, p-value, and error types)
01:12:10 Student's Single Sample T-Test
01:17:12 Student's Two Independent Samples T-Test
01:20:45 Student's Two Paired Samples T-Test
01:32:19 Section 3: (Regression Analysis)
01:33:05 Variance and Standard Deviation
01:34:21 Covariance and Covariance Matrix
01:37:35 Correlation and Correlation Matrix
01:40:57 Regression Analysis
01:48:55 Linear Regression
01:50:22 Fitting a Line (Gradient Descent)
01:58:25 Fitting a Line (Linear Least Squares)
email: [email protected], [email protected], [email protected]
Lecture Slides and Resources: http://arifbutt.me
Jupyter notebooks: https://github.com/arifpucit/data-sci...