Predictive Analytics with TensorFlow 2.2: Basic Probability for Predictive Modeling

The video introduces basic probability concepts, including random variables and their types, generating random numbers, and setting a random seed for reproducibility. It covers probability distributions, both discrete and continuous, and explains marginal and conditional probabilities with examples. The video concludes with an introduction to Bayes' rule and its application in updating probabilities based on known events.

Provide an example of a probability distribution and explain its components.

How does conditional probability differ from marginal probability?

What does Bayes' rule state regarding the probability of events?