ML Fundamentals
Machine Learning
Teaching computers to learn patterns from data and make predictions, without being explicitly programmed for every scenario.
Definition
A subset of AI where algorithms learn patterns from data to make predictions or decisions without being explicitly programmed. Includes supervised, unsupervised, and reinforcement learning paradigms.
Why it matters
The foundation of modern AI, every intelligent system, from spam filters to self-driving cars, uses ML.
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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