Dispersion and spread in data, variance, standard deviation

Dispersion exploration through code

Intro to uncertainty, probability intuition

simulating coin flips for probability

Dispersion exploration through code

Applying conditional probability – Bayes rule

Application of Bayes rule in real world – Spam detection

Spam detection – implementation issues

Rules for counting (Mostly optional)

Quantifying Events – random variables

Two random variables – joint probabilities

Distributions – rationale and importance

Discrete distributions through code

Continuous distributions – probability densities

Continuous distributions code

Case study – sleep analysis, structure and code

Visualizing distributions – the road to ML Success

Dependence and variance of two random variables

Expected values – decision making through probabilities

Entropy – The most important application of expected values

Applying entropy – coding decision trees for machine learning

Foundations of Bayesian inference

Bayesian inference code through PyMC3