ML Fundamentals
K-Means Clustering
Grouping similar things together automatically, like sorting customers into segments based on their behavior.
Definition
An unsupervised learning algorithm that partitions data into K distinct clusters based on similarity. Iteratively assigns points to the nearest cluster center and updates centers until convergence.
Why it matters
One of the most widely used algorithms for customer segmentation, anomaly detection, and data exploration.
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
Ready to put K-Means Clustering to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
Book a Discovery Call