A random variable turns an uncertain outcome into a number, and a probability distribution describes how likely each value is. Almost every AI model rests on this idea: data is treated as samples from a distribution, and models output distributions rather than single answers.
The lessons start with the main ideas behind distributions, then look at two of the most important ones: the normal distribution, the familiar bell curve, and the binomial distribution, which counts successes across repeated trials. They finish with sampling from a distribution and the central limit theorem, which explains why the bell curve shows up so often.
Watch the lessons in order, then continue on to Descriptive Statistics & Correlation.
Explains what a probability distribution is and why it is used to describe uncertain outcomes.
Video 5minIntroduces the bell curve, its mean and standard deviation, and how to read it.
Video 5minCovers the binomial distribution for counting successes in a fixed number of trials, plus the binomial test.
Video 15minShows what drawing samples from a distribution means and how it relates to generating data.
Video 3minExplains why averages of many samples tend toward a normal distribution, whatever the original data looks like.
Video 7min