ML Fundamentals
Decision Trees
An AI that makes predictions by asking a series of yes/no questions, like a flowchart.
Definition
A supervised learning algorithm that splits data into branches based on feature values, creating a tree-like structure of decisions. Highly interpretable and the basis for ensemble methods like Random Forests.
Why it matters
One of the most intuitive ML algorithms, easy to explain to non-technical stakeholders.
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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