will wolf

Roughly speaking, my machine learning journey began on Kaggle. "Regression models predict continuous-valued real numbers; classification models predict'red,' 'green,' 'blue.' Typically, the former employs the mean squared error or mean absolute error; the latter, the cross-entropy loss. Stochastic gradient descent updates the model's parameters to drive these losses down." Furthermore, to fit these models, just import sklearn. A dexterity with the above is often sufficient for -- at least from a technical stance -- both employment and impact as a data scientist. In industry, commonplace prediction and inference problems -- binary churn, credit scoring, product recommendation and A/B testing, for example -- are easily matched with an off-the-shelf algorithm plus proficient data scientist for a measurable boost to the company's bottom line. In a vacuum I think this is fine: the winning driver does not need to know how to build the car.