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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

2,310 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

91%

of learners achieved a positive career outcome

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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

There are 4 modules in this course

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.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Module details

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

3 videos6 readings2 programming assignments1 discussion prompt

3 videosTotal 55 minutes

Text Classification Lecture 124 minutes
Text Classification Lecture 211 minutes
Text Classification Lecture 5 (Repeated in Week 3)19 minutes

6 readingsTotal 221 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for your Work!10 minutes
Course Support10 minutes
Introduction to using Google Colab for this course10 minutes
Python Syntax Review10 minutes
Python Basics & Colab Intro Reading180 minutes

2 programming assignmentsTotal 360 minutes

Python Assessment 1: File I/O180 minutes
Python Assessment 2: Data Structures and Strings180 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself!10 minutes

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

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

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