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  • Specialization:听Text Marketing Analytics
  • Instructor:听Chris J. Vargo, Assistant Professor and Scott Bradley
  • Prior knowledge needed:听None

Learning Outcomes听

  • Describe text classification and related terminology (e.g., supervised machine learning).
  • Apply text classification to marketing data through a peer-graded project.
  • Apply text classification to a variety of popular marketing use cases via structured homeworks.
  • Evaluate, tun, and improve the performance of the text classification models you create for your final project.

Course Content

Duration: 8h

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.

Duration: 1h

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.

Duration: 2h

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鈥檚 Tensorflow and transformer models.

Duration: 1h

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.

Duration: 30m

You will complete a peer reviewed project worth 20% of your grade. You must attempt the final in order to earn a grade in the course.听If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click听View on Coursera听button听above for the most up-to-date information.