Unsupervised Learning Series #1: A Beginner’s Guide to Concepts and Models That Work
Last Updated on April 24, 2025 by Editorial Team
Author(s): SETIA BUDI SUMANDRA
Originally published on Towards AI.
“No Label, No Problem”. That’s the motto of Unsupervised Learning — a fascinating branch of machine learning where algorithms learn patterns from unlabeled data.
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In this first part of our Unsupervised Learning Series, we’ll dive into the core concepts of unsupervised learning, explore useful models that work effectively, and show you how this powerful tool can be applied to real-world problems.
In simpler terms, unsupervised learning works with data that doesn’t have predefined labels — meaning there’s no predefined output to guide the model.
Imagine you have a bunch of customer data, but you don’t know anything about which customers belong to which group. Unsupervised learning helps you automatically discover patterns or groupings or clustering in the data, like identifying clusters of customers with similar behaviors or preferences.
Unlike supervised learning, which requires labeled data to make predictions (like categorizing email as spam or not spam), unsupervised learning helps us explore and gain insights from raw, unlabeled data.
If you interest with modelling & Analysis, feel free to acces another my articles here:
Intro — From 🤯 to 😎
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