Course Introduction
Course Overview
Two Branches of Statistics
Statistics and Machine Learning
Gaussian Distribution
Sample vs Population
Demo: Gaussian Distributions in Python
Measures of Central Tendency
Measures of Variability
Demo: Calculating the Mean and Median in Python
Demo: Variance
Randomness in Machine Learning
Demo: Random Numbers with Python
Demo: Random Numbers with NumPy
When to Seed and Controlling for Randomness
Law of Large Numbers
Central Limit Theory
Demo: Central Limit Theory
Statistical Hypothesis Testing
Defining P-Value
Reject or Fail Null Hypothesis
Errors in Hypothesis Testing
Statistical Distributions
Density Functions
Demo: Probability Density function in Python
Student's T-Distribution
Chi-Squared Distribution and Demo: Chi-Squared Distribution
Critical Values
One-Tailed and Two-Tailed Tests
Demo: Calculating Critical Values
Correlation Defined
Demo: Strong Positive Correlation
Covariance and Covariance Demo
Pearson's R Defined and Demo
Parametric Statistical Tests
Demo: Parametric Significance Tests
Effect Size
Demo: Pearson's Correlation Between Two Variables
Statistical Power Defined
Power Analysis: The Core 4
Demo: Student’s t-Test Power Analysis
Data Sampling and Resampling
Sampling Errors
Statistical Resampling
Bootstrap Approach
Demo: Bootstrap in Python
K-Fold Cross-Validation
Demo: K-Fold Cross Validation
Variations on Cross-Validation
Demo: Train/Test Split
Problems with Hypothesis Testing
Estimation Statistics Defined
Effect Size
Interval Estimation
Tolerance Intervals
Demo: Parametric Tolerance Intervals
Confidence Intervals
Demo: Confidence Intervals
Demo: Non-Parametric Confidence Intervals
Prediction Intervals
Demo: Prediction Intervals Using Linear Regression
