Multiple linear regression. Multiple linear regression models are much more complicated and can work with a greater number of lines and shapes on charts. They're typically employed when there are multiple independent variables -- values that aren't beholden to other numbers in the model -- being put together to get a single answer.
Polynomial regression is a common application and helps tell a better story because it can more closely fit data points outside a single straight line. The growth of the stock market, for example, might be predicted using multiple linear regression.
Logistic regression. In logistics regression, you can use machine learning to help predict the probability of the outcome of a situation with two potentials. For instance, it is good for predicting whether something will be true or false.
It's like a souped-up magic eight-ball in a way but based on significant data that can be used to calculate the likelihood of one or both potential outcomes occurring. So, for example, you might use logistic regression to predict whether a company is a credit risk.