Unsupervised Learning Series #1: A Beginner’s Guide to Concepts and Models That Work

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.

Not part of the Medium’s partner program? No worries! 👉 Read this article here.

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 😎

medium.com

Not part of the Medium’s partner program? No worries! 👉 Read this article… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI