Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.
Unsupervised Text Classification for Marketing Analytics
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Unsupervised Text Classification for Marketing Analytics
This course is part of Text Marketing Analytics Specialization
Instructors: Chris J. Vargo
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
What you'll learn
Describe the concept of topic modeling and related terminology (e.g., unsupervised machine learning)
Apply topic modeling to marketing data via a peer-graded project
Apply topic modeling to a variety of popular marketing use cases via homework assignments
Evaluate, tune and improve the performance the topic model you create for your project
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
5 assignments
Taught in English
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This course is part of the Text Marketing Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Earn a shareable career certificate
There are 5 modules in this course
In this module, we will cover the fundamental concepts of topic modeling, also known as unsupervised machine learning on unstructured text documents. We will contrast unsupervised methods to supervised ones and survey common applications of topic modeling.
What's included
3 videos5 readings2 assignments1 programming assignment1 discussion prompt
In this module, we will go under the hood inside a topic modeling approach and understand what assumptions drive topic model fit. We will also uncover how bag-of-words approaches to topic modeling work, and the natural language processing required to produce meaningful topic modeling features.
What's included
1 reading1 programming assignment
In this module, we will learn how to evaluate the fit of topic models and use the best topic model to classify documents. We will also cover how to build topic models with pre-trained neural networks.
What's included
3 readings1 peer review
Network analysis was created as a science to study the relationships that people have with each other. This module will focus on on social networks, or relational networks of people, things, and so on.
What's included
4 videos2 readings2 assignments
In this module we'll go further into network analysis and study the relationships that words have with each other. Semantic networks can tell is more about the context in which people are using words in collections of documents, and in this module we'll learn how to turn this method into marketing insights.
What's included
2 readings1 assignment2 programming assignments
Earn a career certificate
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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