Day 4 of 180 | Unsupervised Model in Machine Learning | What is Unsupervised Learning day 4
In this Hindi‑language lecture, Akansha Yadav explains the core idea of unsupervised learning. She starts with a puzzle‑piece analogy: just as we can assemble a picture without knowing the final image, unsupervised models receive raw, unlabeled data and must discover underlying patterns on their own. She emphasizes that the learner visualizes the data, looks for recurring structures, and decides how to group or represent them. The talk highlights that no explicit labels are provided, making the task fundamentally different from supervised learning. Akansha briefly mentions common unsupervised techniques—such as clustering (e.g., K‑means, hierarchical clustering) and other model families—while noting the wide range of applications where pattern discovery is valuable. The overall tone is instructional and enthusiastic, aiming to make the abstract concept accessible through everyday analogies.
