Unsupervised Text Classification for Marketing Analytics
Master topic modeling and unsupervised machine learning techniques for analyzing marketing text data.
Master topic modeling and unsupervised machine learning techniques for analyzing marketing text data.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Text Marketing Analytics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Apply topic modeling to extract insights from marketing data
Implement unsupervised machine learning techniques
Preprocess and analyze large text datasets
Evaluate and tune topic model performance
Utilize advanced neural network approaches
Skills you'll gain
This course includes:
2.2 Hours PreRecorded video
2 quizzes, 2 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course focuses on unsupervised deep learning techniques for analyzing large-scale marketing text data. Students learn to implement topic modeling and other unsupervised machine learning approaches using Python. The curriculum covers fundamental concepts, data preprocessing, model training, and evaluation. Through hands-on projects with real-world datasets, learners master techniques for extracting meaningful insights from unstructured text data using advanced tools like BERT and neural networks.
What is topic modeling?
Module 1 · 4 Hours to complete
The Assumptions of a Topic Model, Bag of Words, and Natural Language Processing
Module 2 · 4 Hours to complete
Prepping Amazon Review Data
Module 3 · 1 Hours to complete
Pre-Processing Text and Training a Topic Model
Module 4 · 1 Hours to complete
Topic Modeling Evaluation, Classification, and Neural Network Approaches
Module 5 · 2 Hours to complete
Fee Structure
Instructors
Associate Professor
Dr. Chris J. Vargo is an Associate Professor at the University of Colorado Boulder, specializing in data analytics and digital advertising. His research integrates computer science techniques to analyze social media data through the lenses of communication, psychology, and political science. With expertise in data mining, machine learning, and predictive analytics, Dr. Vargo employs advanced methodologies such as network analysis and information retrieval to investigate contemporary media landscapes. His goal is to enhance the quantitative analytical skills of students, preparing them for the demands of the industry.In the classroom, Dr. Vargo teaches various courses related to advertising analytics at both undergraduate and graduate levels, including "Introduction to Digital Advertising" and "Programmatic Advertising." He also directs the CMCI/Leeds Marketing and Business Analytics partnership, fostering collaboration between departments. His scholarly contributions have been published in notable journals such as the Journal of Communication and New Media & Society. Additionally, he serves as the Editor of The Agenda Setting Journal, focusing on agenda-setting theory in new media contexts. With a robust academic background that includes a Ph.D. from The University of North Carolina at Chapel Hill and practical experience in public relations and digital marketing, Chris J. Vargo is dedicated to advancing research and education in the rapidly evolving field of digital advertising.
Senior Engineer
Scott Bradley is an Instructor at the University of Colorado Boulder, specializing in marketing analytics and data science. He teaches several courses, including "Network Analysis for Marketing Analytics," "Supervised Text Classification for Marketing Analytics," and "Unsupervised Text Classification for Marketing Analytics." His expertise lies in applying advanced analytical techniques to enhance marketing strategies and improve decision-making processes within organizations.With a strong background in data analysis and machine learning, Scott combines theoretical knowledge with practical applications to equip students with the skills necessary for success in the rapidly evolving field of data science. His courses focus on the intersection of data analytics and marketing, providing students with valuable insights into how data can drive effective marketing strategies. Through his teaching, Scott Bradley plays a crucial role in preparing the next generation of data-driven marketers at CU Boulder.
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