Unsupervised Learning: Algorithms and Business Use Cases
Understand how unsupervised learning algorithms like clustering and principal component analysis help uncover patterns in data without labeled outcomes. Learn how to apply these techniques to real business problems including customer segmentation, recommendation systems, image analysis, and anomaly detection, gaining insight into reducing data complexity and enhancing machine learning outcomes.
We'll cover the following...
Clustering
Understanding customers is important for businesses. Usually different groups of users can be split across some criteria or into different groups. The criteria or grouping can be as simple as age and gender or as complex as personality type and purchase history.
Clustering algorithms are able to go through the data and find these natural clusters if they exist; they can segregate groups with similar traits and assign them into clusters. For example, clustering might create one cluster of retired people who own a pet and another of millennials who play guitar. We can typically define the number of clusters we want; this allows us to adjust the granularity of these groups.
There are a few different types of clustering algorithms we can utilize. The algorithms differ ...