Assume your data came from a probability model with unknown parameters. Estimation means guessing those parameters from the data.

Example: you flip a coin many times and want P(heads). Assume a model (a coin has some fixed p), then estimate p from what you observed.

This "assume a model, estimate its parameters" pattern shows up constantly for the rest of the course.