Predictive Analytics and Machine Learning Module 5: Text Classification and Topic Modeling (Synchronous e-Learning)

Text data are common and useful data sources of machine learning problems in real-world predictive modeling. Building on the previous four modules, this module extends supervised and unsupervised learning to text data (e.g., customer or employee reviews, speeches, writings, tweets of organization leaders, and news articles). Participants will learn how to execute predictive models when given prediction tasks dealing with text. Beyond supervised learning of text data, the module provides guidance on unsupervised learning on text data with topic modeling to discover clusters (natural groups) within them. More importantly, participants will learn how to pre-process text data, feeding features developed from text mining into modeling pipelines. They will learn about natural language features (e.g., tokenization, stop words, stemming, word embeddings) and how they can be used in executing text classification and topic modeling.

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