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Linear Regression is a common machine learning technique that predicts a real-valued output using a weighted linear combination of one or more input values. The “learning” part of linear regression is to figure out a set of weights that leads to good predictions. This is done by looking at lots of examples one by one (or in batches) and adjusting the weights slightly each time to make better predictions. We’ll assume that is a linear function of, with some noise added to account for features we haven’t considered. Here’s how the model weights look like right now: