Clustering in NLP

4 hours of instruction

This course covers the clustering concepts of natural language processing, equipping learners with the ability to cluster text data into groups and topics by finding similarities between different documents.

OBJECTIVES

1. Understand measures of similarity and distance
2. Learn and implement cosine similarity on text documents
3. Understand how similar documents can be clustered into topics

PREREQUISITES

Topic Modeling in NLP

SYLLABUS & TOPICS COVERED

1. Cosine Similarity
   • Measures of similarity and distance
   • Theory and implementation of cosine similarity find most similar documents
2. Clustering Documents
   • Clustering as an unsupervised method in text analysis
   • Hierarchical clustering algorithm in a nutshell
   • How to implement clustering on a corpus of documents

SOFTWARE REQUIREMENTS

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