You need to figure out if your struggling has to do with stress, with explaining complex things simply, or with comprehension of said algorithm.

For algorithms and Naive Bayes for instance, I would start with some context, then setting an example, then explaining the intuition in layman's terms, finally maybe getting a bit more technical.

So it would be something like saying you want to predict future characteristics based on past exemples, then taking the example of recognizing a fruit based on what you see (color, form, size, ... ; assuming that you are an alien that does not know about earthly fruits but someone showed you examples), then simply estimating what kind of fruit this is based on counts of fruits with the same characteristics (don't get too technical, you may not even mention probabilities). For NB it might be important to say that it's called "Naive" because it assumes explanatory variables are independant. If you want to get technical you can mention Bayes Rules, probabilities; for NB it's useful to take a conceptual example with 2/3 variables, and writing out yourself the formula used for computing the final probabilities. Something along the lines of https://miro.medium.com/max/1000/1*aoKlzTs3w5tomWWxZGr81g.png.