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

Supervised 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

3.4

(10 reviews)

Beginner level

Recommended experience

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

What you'll learn

  • 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

  • Train, evaluate and improve the performance of the text classification models you create for your final project

Details to know

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Assessments

3 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.4

(10 reviews)

Beginner level

Recommended experience

12 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 4 modules in this course

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.

What's included

4 videos4 readings2 programming assignments1 discussion prompt

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.

What's included

2 videos2 readings1 quiz

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.

What's included

2 videos2 readings1 quiz

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.

What's included

2 videos2 readings1 quiz

Instructors

Chris J. Vargo
University of Colorado Boulder
7 Courses64,202 learners

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