Network Analysis for Marketing Analytics

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Network Analysis for Marketing Analytics

This course is part of Text Marketing Analytics Specialization

Instructors: Chris J. Vargo

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6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

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

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

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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 6 modules in this course

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.

Let's get situated with the course.

What's included

5 readings1 discussion prompt1 ungraded lab

Let's start by reviewing some advanced concepts in supervised machine learning and how they apply to building state of the art classification models for real world marketing purposes

What's included

1 video2 assignments1 ungraded lab

We'll tackle using k-train, a lightweight Google TensorFlow wrapper, to build a custom model that classifies data based on context. These lightweight models may not perform as well as large language models, but they are lightweight and can be highly effective.

What's included

1 reading2 assignments2 ungraded labs

Moving further down the model size and technological advancement line, we will introduce generative AI, and how these very large models can help us classify data.

What's included

3 videos2 readings3 assignments1 ungraded lab

3 videosTotal 11 minutes

Generative AI for Text Classification: Overview4 minutes
How Language Models Learn: Next-Token and Masked Prediction4 minutes
Word Embeddings and Context3 minutes

2 readingsTotal 190 minutes

Google's Introduction to Generative AI10 minutes
LLM Fundamentals and Theory180 minutes

3 assignmentsTotal 90 minutes

Google's Intro to Generative AI30 minutes
Generative AI Foundations Mastery Check30 minutes
LLM Foundations and Theory30 minutes

1 ungraded labTotal 60 minutes

LLM Fundamentals60 minutes

Use large language models that are open source and free to all to classify data and solve problems. We will focus on smaller models that fit inside of Google Colab and are free to anyone with a Google account!

What's included

3 videos3 readings3 assignments3 ungraded labs

3 videosTotal 10 minutes

From Representations to Classification3 minutes
Prompt Contracts for Reliable Classification4 minutes
From a Checked Prototype to Batch API Labeling3 minutes

3 readingsTotal 540 minutes

LLM Inferencing180 minutes
vLLM180 minutes
Evaluation and Ethics180 minutes

3 assignmentsTotal 90 minutes

LLM Inferencing30 minutes
vllm. 30 minutes
Evaluation and Ethics30 minutes

3 ungraded labsTotal 180 minutes

LLM Inferencing60 minutes
vLLM60 minutes
Evaluation and Ethics60 minutes

In this module we'll learn how to make LLMs of our own by fine tuning models to our specific use cases. By taking models that are already generally smart and adapting them to our examples of known inputs and outputs, we can quickly build highly intelligent classifiers that solve specific, tricky problems!

What's included

1 video3 readings2 assignments2 ungraded labs

1 videoTotal 4 minutes

When Fine-Tuning Fails: Label Audits and Classwise F14 minutes

3 readingsTotal 370 minutes

Scores, Rationales, Batch Workflows, and Evaluation10 minutes
Fine Tuning via Unsloth180 minutes
Production and Deployment180 minutes

2 assignmentsTotal 60 minutes

Fine Tuning via unsloth30 minutes
Production and Deployment30 minutes

2 ungraded labsTotal 120 minutes

Fine Tuning via Unsloth60 minutes
Production and Deployment60 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

Chris J. Vargo

University of Colorado Boulder

7 Courses82,085 learners

Scott Bradley

University of Colorado Boulder

3 Courses3,137 learners

Offered by

University of Colorado Boulder

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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.

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