[Test0129]

You are looking at it from a programmer standpoint rather than a mathematical standpoint.

Linear regression isn't just fitting a line, it's a statistical technique to fit a line of best fit. Hyperparameters are a bayesian term for parameters outside the system of test or "algorithm". User input really misses the bayesian aspect.

These terms actually have meaning so I'd be careful ascribe simpler definitions. The underlying meaning is important to the reason they work. If you don't have a really strong background in probability theory and statistics trying to dig into machine learning will take work. Id recommend taking an MITx course or picking up a textbook on probability so the terminology feels more natural.