Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.
Supervised Text Classification for Marketing Analytics
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Supervised Text Classification for Marketing Analytics
This course is part of Text Marketing Analytics Specialization
Instructors: Chris J. Vargo
2,112 already enrolled
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Gain insight into a topic and learn the fundamentals.
3.1
(14 reviews)
Beginner level
Recommended experience
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
Build toward 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
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
7 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.
- Learn new concepts from industry experts
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- Earn a shareable career certificate
There are 4 modules in this course
Let's get started with the basics of Google Colab and Jupyter Notebooks
What's included
2 videos5 readings2 assignments1 discussion prompt
Some problems in basic information retrieval require simple approaches, like looking for specific keywords and token in text. We'll start with the basics of information science and systems and talk about some simple but powerful tools in natural language processing.
What's included
1 video1 reading2 assignments
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
1 video1 reading2 assignments2 programming assignments
In this module, you will conclude the course with one final assignment!
What's included
2 readings1 assignment
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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