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
Supervised Text Classification for Marketing Analytics
This course is part of Text Marketing Analytics Specialization
Instructors: Chris J. Vargo
Sponsored by BrightStar Care
1,852 already enrolled
Gain insight into a topic and learn the fundamentals.
3.1
(14 reviews)
Beginner level
Recommended experience
12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
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
Shareable certificate
Add to your LinkedIn profile
Assessments
3 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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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 assignment
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 assignment
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 assignment
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