Episode 25 - Unsupervised Learning

In Episode 25 of '100 Days of Data,' Jonas and Amy unpack the fundamentals of unsupervised learning, a branch of machine learning where algorithms find structure in unlabeled data. They explore key techniques like clustering and dimensionality reduction, illustrating their real-world applications in customer segmentation, fraud detection, and genetic research. With relatable examples from retail, finance, and healthcare, the hosts show how unsupervised learning empowers AI to uncover patterns that humans may overlook. They also discuss common algorithms such as k-means and PCA, along with the importance of domain expertise in interpreting results. The episode emphasizes how unsupervised learning's exploratory nature opens new doors for businesses dealing with complex, unlabeled datasets.