AI building blocks - from scratch with Python
Linear regression and gradient descent are techniques that form the basis of many other, more complicated, ML/AI techniques (e.g., deep learning models). They are, thus, building blocks that all ML/AI engineers need to understand.
Regression is a process in which we model how one variable (for example, sales) changes with respect to another variable (for example, number of users). Generally, regression techniques in machine learning are concerned with predicting continuous values (for example, stock price, temperature, or disease progression). Classification, on the other hand, is concerned with predicting discrete variables, or one of a discrete set of categories (for example, fraud/not fraud, sitting/standing/running, or hot dog/not hot dog).
Linear regression, as you might expect, models the relationship a response (e.g., sales) and a feature (e.g., users) using the...
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