The Math Behind Machine Learning
Explore how supervised learning uses mathematical approximation to map input data to output labels. This lesson explains the training and prediction phases using a solar power example, helping you grasp how machine learning models understand and predict real-world data patterns.
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A supervised learning system exploits a mathematical concept to understand the relationship between a piece of data and its label. It is the idea of approximating a function. Let’s see how that idea works, with a concrete example.
Example of solar power prediction
Let’s imagine that we have a solar panel on our roof. We want a supervised learning system that learns how the solar panel generates energy and predicts the amount of energy generated at some time in the future.
There are a few variables that impact the solar panel’s output: the time of day, the weather, and so on. The time of day looks like an important variable, so we decide to focus on that one. In true supervised learning fashion, we ...