Predictive Analytics and Machine Learning Module 4: K-Means and Hierarchical Clustering (synchronous E-Learning)

At the end of the 2-day module, participants will be able to:

• Understand the essential concepts of K-means and hierarchical clustering.
• Understand what types of problems can be solved by K-means and hierarchical clustering.
• Execute K-means and hierarchical clustering using Tidy data principles.
• Know the similarities and differences between dimensionality reduction and clustering, and their relation to supervised learning.

What You'll Learn

Building on a previous module on dimensionality reduction, the module will further develop participants' expertise in unsupervised learning with K-means and hierarchical clustering. They will learn the core concepts of K-means and hierarchical clustering, and how they are related to but differ from dimensionality reduction (here, PCA), and when they can aid supervised learning. Participants will learn how to execute clustering by applying Tidy data principles and solving real-world business problems (e.g., market segmentation, customer journey mapping, investor clustering).

Minimum Entry Requirement

Participants with at least a diploma qualification and completed Certified Data Analytics (R) Specialist or equivalent