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Machine Learning and Generative AI for Marketing

You're reading from Machine Learning and Generative AI for Marketing Take your data-driven marketing strategies to the next level using Python

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835889404

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

Combine

Concepts

Artificial Intelligence

Authors (2):

Nicholas C. Burtch

Yoon Hyup Hwang

View More author details

Table of Contents (16) Chapters

Preface

1. The Evolution of Marketing in the AI Era and Preparing Your Toolkit FREE CHAPTER

2. Decoding Marketing Performance with KPIs

3. Unveiling the Dynamics of Marketing Success

4. Harnessing Seasonality and Trends for Strategic Planning

5. Enhancing Customer Insight with Sentiment Analysis

6. Leveraging Predictive Analytics and A/B Testing for Customer Engagement

7. Personalized Product Recommendations

8. Segmenting Customers with Machine Learning

9. Creating Compelling Content with Zero-Shot Learning

10. Enhancing Brand Presence with Few-Shot Learning and Transfer Learning

11. Micro-Targeting with Retrieval-Augmented Generation

12. The Future Landscape of AI and ML in Marketing

13. Ethics and Governance in AI-Enabled Marketing

14. Other Books You May Enjoy

15. Index

Index

A

A/B testing 11, 213

conducting, for optimal model choice 213, 214

simulating 214-218

two-tailed t-test 219, 220

Additive time-series decomposition method 120-124

AI advancements 145

AI marketing models

bias, mitigating strategies 417

AI/ML, for marketing 396

bias, mitigating 416-419

ethical considerations 410

ethics and governance 168

explainability 410, 411

model architecture advances 398

model explainability tools 411-416

multi-modal GenAI 401

privacy with personalization, balancing 420-426

Python environment, setting up for 13

Python libraries, installing for 14

ReAct 396

transparency 410

Akaike Information Criterion (AIC) 131

Anaconda distribution

installing 13

anomaly attribution 102-105

anonymization 421

API services

using, for transfer learning 340, 341

applications in marketing, ReAct

dynamic pricing adjustments 397

interactive customer service...

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Authors (2)

Yoon Hyup Hwang

Yoon Hyup Hwang

Yoon Hyup Hwang is a seasoned data scientist in the marketing and financial sectors with expertise in predictive modeling, machine learning, statistical analysis, and data engineering. He has 8+ years' experience of building numerous machine learning models and data products using Python and R. He holds an MSE in computer and information technology from the University of Pennsylvania and a BA in economics from the University of Chicago. In his spare time, he enjoys practicing various martial arts, snowboarding, and roasting coffee. Born and raised in Busan, South Korea, he currently works in New York and lives in New Jersey with his artist wife, Sunyoung, and a playful dog, Dali (named after Salvador Dali).

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Nicholas C. Burtch

Nicholas C. Burtch

Nicholas C. Burtch, PhD, is a recognized data science researcher and thought leader with over ten years of experience in leading complex, data-driven projects. He has an extensive track record of deploying end-to-end ML solutions for understanding large-scale structured and unstructured data in industries ranging from finance to scientific research. Nick has published dozens of peer-reviewed research articles that have received thousands of citations and is a US patent holder. He received his PhD and MS in Chemical Engineering from the Georgia Institute of Technology and holds a BS in Chemical Engineering from the University of Michigan.

See other products by Nicholas C. Burtch

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