University of Colorado Boulder
Unsupervised Text Classification for Marketing Analytics
University of Colorado Boulder

Unsupervised Text Classification for Marketing Analytics

This course is part of Text Marketing Analytics Specialization

Taught in English

Chris J. Vargo
Scott Bradley

Instructors: Chris J. Vargo

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards 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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Text Marketing Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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

1 video4 readings1 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

2 videos1 reading1 quiz1 programming assignment

In this module, we will cover how to parse through JSON-like data and segment it to create a corpus that is ready for the topic modeling process. We will cover how the data for your project is structured and its taxonomy.

What's included

2 videos2 readings1 quiz

In this module, we will take Amazon review data and load it into a corpus to preprocess it. We will cover how to build topic models from the data and also save those topic models.

What's included

2 videos2 readings1 peer review

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 videos3 readings1 peer review

Instructors

Chris J. Vargo
University of Colorado Boulder
7 Courses64,986 learners
Scott Bradley
University of Colorado Boulder
3 Courses2,117 learners

Offered by

Recommended if you're interested in Data Analysis

Get a head start on your degree

This course is part of the following degree programs offered by University of Colorado Boulder. If you are admitted and enroll, your coursework can count toward your degree learning and your progress can transfer with you.

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