Often, the data you encounter in the real world won’t have flags attached and won’t provide labeled answers to your question. Finding patterns in this type of data, unlabeled data, is a common theme in many machine learning applications. Unsupervised Learning is how we find patterns and structure in these data.

Clustering is the most well-known unsupervised learning technique. It finds structure in unlabeled data by identifying similar groups, or clusters. Examples of clustering applications are:

• Recommendation engines: group products to personalize the user experience
• Search engines: group news topics and search results
• Market segmentation: group customers based on geography, demography, and behaviors
• Image segmentation: medical imaging or road scene segmentation on self-driving cars

Let’s get started!