the intercept can then be computed as the mean of the minus the slope times the mean of againeven though that' really not that difficultpython will do it all for youbut the point is that these aren' complicated things to run they can actually be done very efficiently remember that least squares minimize the sum of squared errors from each point to the line another way of thinking about linear regression is that you're defining line that represents the maximum likelihood of an observation line therethat isthe maximum probability of the value being something for given value people sometimes call linear regression maximum likelihood estimationand it' just another example of people giving fancy name to something that' very simpleso if you hear someone talk about maximum likelihood estimationthey're really talking about regression they're just trying to sound really smart but now you know that term tooso you too can sound smart the gradient descent technique there is more than one way to do linear regression we've talked about ordinary least squares as being simple way of fitting line to set of databut there are other techniques as wellgradient descent being one of themand it works best in three-dimensional data soit tries to follow the contours of the data for you it' very fancy and obviously little bit more computationally expensivebut python does make it easy for you to try it out if you want to compare it to ordinary least squares using the gradient descent technique can make sense when dealing with data usually thoughleast squares is perfectly good choice for doing linear regressionand it' always legitimate thing to dobut if you do run into gradient descentyou will know that that is just an alternate way of doing linear regressionand it' usually seen in higher dimensional data