How can we design smarter learning systems that respond to learners’ individual goals and backgrounds?

In this talk, I’ll share how I built a lightweight yet effective personalized course recommendation engine for an e-learning platform using unsupervised learning. Starting with real questionnaire submissions from users interested in Data Analytics, I applied clustering to segment learners by intent, experience, and pain points. Based on these segments, I matched them with curated learning paths from a library of course offerings. The whole system is build using Python and related libraries.

This isn’t a deep-learning black box. It’s a lean, explainable approach that uses scikit-learn pipelines and KMeans clustering to bridge user intent with actionable content. The result? A system that adapts to each learner and scales with minimal manual effort.

This talk will walk the audience through:

1) The challenge of personalization in learning platforms,
2) How clustering can help reveal patterns in learner behavior,
3) Lessons learned building for real users on a live platform.

By the end, you’ll see how unsupervised learning, when thoughtfully design, can create scalable, human-centered experiences, especially in education.