Introduction to Unsupervised Learning
Learn how unsupervised learning uncovers hidden patterns in unlabeled data by exploring clustering algorithms such as k-means and association rule mining. Understand key steps from data preprocessing to model evaluation and result interpretation, enabling you to apply these techniques to real-world scenarios like customer segmentation and market basket analysis.
Unsupervised learning helps uncover hidden patterns and groupings in data without relying on labeled outputs. In this lesson, we’ll explore the core concepts behind unsupervised learning, apply it to customer segmentation scenarios, and review clustering techniques like k-means to understand their practical strengths and limitations. Let’s get started.
What is unsupervised learning?
Unsupervised learning involves working with datasets that do not have labeled outcomes. Explain the concept of unsupervised learning and how it differs fundamentally from supervised learning approaches.
Sample answer
Unsupervised learning involves training a machine learning model on unlabeled data, allowing the model to find patterns and relationships without predefined labels. This contrasts with supervised learning, which relies on labeled data to map inputs to specific outputs. Supervised learning is task-oriented (e.g., predicting sales), while unsupervised learning is exploratory, uncovering hidden structures in data (e.g., grouping customers based on behavior).
Clustering is a fundamental technique within the broader context of unsupervised learning. As one of its primary applications, clustering helps discover inherent groupings or patterns in data without relying on labeled outputs. By grouping similar data points based on their shared characteristics, clustering provides valuable insights into the underlying structure of datasets. This is useful in exploratory data analysis, where the goal is to better understand the data and generate hypotheses. Within the framework of unsupervised learning, clustering serves as a means to organize complex datasets into interpretable subsets, facilitating downstream tasks such as targeted marketing, ...