Introduction to Clustering in Python

4 hours of instruction

Clustering is a machine learning technique that can be used to group unlabeled data based on shared features. In this course, learners will identify use cases for clustering algorithms and become familiar with the theoretical underpinnings of unsupervised machine learning (working with unlabeled data). In particular, learners will build, evaluate, and interpret a K-means model in Python, based on one of the most commonly used clustering algorithms.

OBJECTIVES

1. Mine data to find latent patterns and groups on numerical data using K-Means clustering
2. Evaluate the accuracy and effectiveness of clustering
3. Identify use cases where clustering analyses are relevant and where they are not applicable

PREREQUISITES

Learners must be comfortable using Python to manipulate data and must know how to create basic visualizations.

SYLLABUS & TOPICS COVERED

1. K-Means
   • Unsupervised learning and its use cases
   • The theory behind K-Means algorithm
   • Implementation of K-Means on a dataset

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.