Generative AI for Marketing: Advanced & Responsible

Übersicht

This course focuses on leveraging generative AI to transform modern marketing strategies through advanced techniques and responsible innovation. It highlights the growing role of AI in creating impactful, data-driven campaigns while ensuring ethical and sustainable practices in the professional landscape.

Learners will gain hands-on understanding of zero-shot and few-shot learning, transfer learning, and retrieval-augmented generation to craft compelling content and micro-target campaigns. By applying these techniques, participants will be able to enhance brand presence and deliver highly personalized marketing experiences.

What distinguishes this course is its integration of cutting-edge AI methodologies with real-world marketing use cases. It not only explains the technical foundations but also demonstrates how to apply them effectively in dynamic business environments.

This course is ideal for marketing professionals, data practitioners, and business leaders looking to adopt AI-driven strategies. A foundational understanding of marketing concepts and basic familiarity with AI or machine learning is recommended.

This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Lehrplan

• Creating Compelling Content with Zero-Shot Learning
This module explores advanced techniques in AI-driven content creation, including zero-shot learning, generative adversarial networks, and long short-term memory networks. Learners will discover how these models enhance data augmentation, semantic understanding, and personalized content generation across various domains. Practical examples, such as generating product descriptions, illustrate the real-world impact of these technologies.
• Enhancing Brand Presence with Few-Shot Learning and Transfer Learning
This module explores how few-shot learning and transfer learning can be leveraged to enhance brand presence through data-efficient marketing strategies. Learners will discover practical frameworks for implementing these AI techniques, analyze their challenges, and apply them to real-world marketing scenarios such as email campaigns and image classification. The module also covers the use of API services and iterative refinement based on campaign metrics.
• Micro-Targeting with Retrieval-Augmented Generation
This module explores how retrieval-augmented generation (RAG) can be leveraged for precision marketing, focusing on the importance of data specificity and real-time content personalization. Learners will gain hands-on experience integrating Elasticsearch and LangChain with large language models to deliver targeted marketing messages. Practical applications, such as optimizing product discounts based on user behavior, are also covered.
• The Future Landscape of AI/ML in Marketing
This module explores the latest advancements in artificial intelligence and machine learning as they transform marketing strategies. Learners will discover how diffusion models, multi-modal architectures, and immersive technologies like AR and VR are shaping the future of digital marketing. By the end, you'll understand both the technical foundations and practical applications of these emerging tools.
• Ethics and Governance in AI-Enabled Marketing
This module explores the ethical challenges and governance strategies associated with AI-driven marketing, including bias mitigation, privacy protection, and policy development. Learners will gain practical insights into ensuring fairness, transparency, and responsible AI use in marketing campaigns.

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Unterrichtet von

Packt - Course Instructors

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Fachgebiete

Artificial Intelligence