ML Fundamentals
Supervised Learning
Training an AI with labeled examples, showing it the right answers so it can learn to predict them on its own.
Definition
A machine learning paradigm where the model learns from labeled training data (input-output pairs). The model learns a mapping function from inputs to outputs. Includes classification and regression.
Why it matters
The most widely used ML paradigm, powers most business applications from churn prediction to deal scoring.
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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