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URL: https://www.coursera.org/learn/supervised-text-classification-for-marketing-analytics

⇱ Supervised Text Classification for Marketing Analytics | Coursera

Supervised Text Classification for Marketing Analytics

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Supervised Text Classification for Marketing Analytics

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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
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

Details to know

Shareable certificate

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Assessments

3 assignments

Taught in English

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.

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 videos•Total 55 minutes
  • Text Classification Lecture 1•24 minutes
  • Text Classification Lecture 2•11 minutes
  • Text Classification Lecture 5 (Repeated in Week 3)•19 minutes
6 readings•Total 221 minutes
  • Course Updates and Accessibility Support•1 minute
  • Earn Academic Credit for your Work!•10 minutes
  • Course Support•10 minutes
  • Introduction to using Google Colab for this course•10 minutes
  • Python Syntax Review•10 minutes
  • Python Basics & Colab Intro Reading•180 minutes
2 programming assignments•Total 360 minutes
  • Python Assessment 1: File I/O•180 minutes
  • Python Assessment 2: Data Structures and Strings•180 minutes
1 discussion prompt•Total 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

2 videos•Total 39 minutes
  • Text Classification Lecture 3•15 minutes
  • Text Classification Lecture 4•24 minutes
2 readings•Total 20 minutes
  • An Example Codebook from Dr. Vargo•10 minutes
  • An Example Paper from Dr. Vargo •10 minutes
1 assignment•Total 180 minutes
  • Supervised Text Classification•180 minutes

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

2 videos•Total 67 minutes
  • Text Classification Lecture 5•19 minutes
  • Text Classification Lecture 6•48 minutes
2 readings•Total 20 minutes
  • Lecture Notebook Links •10 minutes
  • Coding Lab 1: Data Preparation with Pandas•10 minutes
1 assignment•Total 30 minutes
  • Lab 1 Quiz•30 minutes

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

2 videos•Total 17 minutes
  • Text Classification Lecture 7•9 minutes
  • Text Classification Lecture 8•9 minutes
2 readings•Total 20 minutes
  • Lecture Notebook Links•10 minutes
  • Coding Lab 2: Building a Model with K-Train•10 minutes
1 assignment•Total 30 minutes
  • Lab 2 Quiz•30 minutes

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

Instructors

University of Colorado Boulder
7 Courses•81,805 learners

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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