Objectives On completion of this tutorial, you will be able to:

• Define unsupervised machine learning and recall the best approach to using it effectively
• Define cluster analysis and compare the processes, uses, and limitations of hierarchical and non-hierarchical clustering
• Define dimension reduction and identify its uses and limitations

Tutorial Overview When faced with large, unstructured, and unlabeled datasets, many companies use unsupervised machine learning (U-ML) to discover patterns and identify previously unknown factors that may drive business outcomes. While U-ML is a potentially powerful tool, it is important to understand its limitations and the relative advantages of different approaches to it. This tutorial provides a high-level overview of unsupervised machine learning and highlights key factors to consider when using U-ML to solve business problems. Prerequisite Knowledge Supervised Machine Learning In Practice Tutorial Level: Intermediate Tutorial Duration: 45 minutes