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Supervised Text Classification for Marketing Analytics

University of Colorado Boulder via Coursera

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Overview

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Build reliable supervised classifiers from marketing text and defensible human labels. Learners create coding rules, reconcile coders into gold-standard labels, transform text into predictive features, train a regularized elastic-net model, separate training from validation evidence, and use errors and learning curves to judge model quality and the value of collecting more labeled data.

Syllabus

The Supervised Machine Learning Workflow

In this module, we will learn about the different types of machine learning that exist and the operational steps of building a supervised machine learning model. We will also cover performance metrics of text classification.

Neural Networks and Deep Learning

In this module, we will learn about neural networks and supervised machine learning. Then we will dive into real supervised machine learning projects and the key decisions that need to be made when conducting one's own project.

Getting Started with Google Colab and Deep Learning

In this module, we will learn how to work in the Google Colab and Google Drive environment. We will get started with supervised learning by using a wrapper for Google’s Tensorflow and transformer models.

Linear Models and Classification Metrics

In this module, we will learn how to workshop a variety of supervised machine learning models that rely on linear-based models. We will also learn how to perform an external performance analysis of models in sci-kit learn.