Use generative AI and large language models as auditable classifiers for marketing text. Learners connect language-model training and contextual embeddings to classification, design strict prompts and machine-readable label contracts, validate outputs before batch inference, compare prompting with fine-tuning, and evaluate results against audited gold-standard labels using accuracy, macro F1, and class-level errors.

Network Analysis for Marketing Analytics

Network Analysis for Marketing Analytics
This course is part of Text Marketing Analytics Specialization


Instructors: Chris J. Vargo
Access provided by Skills Development Fund
Recommended experience
What you'll learn
Explain how next-token prediction, masked-language modeling, and contextual embeddings support LLM classification.
Translate a marketing research question into a closed label set with definitions and borderline-case rules, and design prompts that return exactly one valid, machine-readable classification label.
Validate a sample, execute efficient batch inference through an API or vLLM workflow, and select an appropriate fine-tuning workflow when prompt-based classification is insufficient.
Evaluate prompting and fine-tuning with accuracy, macro F1, class-level errors, and audits of disputed gold labels.
Skills you'll gain
Tools you'll learn
Details to know

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There are 6 modules in this course
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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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University of Colorado Boulder

University of Colorado Boulder
