AI Marketing Strategy Analytics Course Syllabus

Overview: This course provides a comprehensive introduction to AI-driven marketing strategy and analytics, designed for intermediate learners. Through six structured modules, participants will explore core AI concepts and their practical applications in marketing, including neural networks, natural language processing, and computer vision. The program blends theoretical knowledge with hands-on exercises, case studies, and real-world project work, requiring approximately 18–20 hours of total effort. Learners will gain the skills to make data-driven marketing decisions, evaluate AI models, and design effective campaigns using AI tools.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Introduction to computational thinking
• Applying algorithms to marketing problems
• Basics of data structures and processing
• Hands-on problem-solving techniques

Module 2: Neural Networks & Deep Learning

Estimated time: 4 hours

• Core concepts of neural networks
• Deep learning fundamentals
• Applying deep learning to customer behavior analysis
• Hands-on exercises with real-world datasets

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Key concepts in AI system design
• Scalable algorithm design
• Best practices and industry standards
• Case study analysis of AI marketing systems

Module 4: Natural Language Processing

Estimated time: 3 hours

• Transformer architectures and attention mechanisms
• Prompt engineering for large language models
• Applications in marketing content and sentiment analysis
• Tools and frameworks for NLP

Module 5: Computer Vision & Pattern Recognition

Estimated time: 4 hours

• Introduction to computer vision in marketing
• Pattern recognition for customer insights
• Hands-on image and video analysis techniques
• Real-world case studies and applications

Module 6: Deployment & Production Systems

Estimated time: 2 hours

• Deploying AI models in marketing environments
• Monitoring and maintaining performance
• Final project: Implementing an AI marketing analytics solution

Prerequisites

• Familiarity with basic marketing concepts
• Basic understanding of data analysis
• Some experience with programming or analytics tools recommended

What You'll Be Able to Do After

• Evaluate AI model performance using marketing-relevant metrics
• Apply computational thinking to marketing challenges
• Design scalable AI-driven marketing strategies
• Utilize NLP and computer vision for customer insights
• Deploy AI solutions in real-world marketing contexts