ML Fundamentals
Regression
Teaching an AI to predict a number, like a home's price, a deal's close probability, or next quarter's revenue.
Definition
A supervised learning task where the model predicts a continuous numerical output. Linear regression, polynomial regression, and neural network regression are common approaches.
Why it matters
Powers revenue forecasting, pricing optimization, demand prediction, and virtually every quantitative business model.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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