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Course Outline
Introduction to Multimodal AI
- What is multimodal AI?
- How multimodal AI models work
- Use cases in various industries
Prompt Engineering Fundamentals
- Principles of effective prompt design
- Understanding AI response behavior
- Common mistakes and how to avoid them
Text-Based Prompt Optimization
- Structuring prompts for accurate text generation
- Fine-tuning responses for different contexts
- Handling ambiguity and bias in text prompts
Image Generation and Manipulation
- Optimizing prompts for AI-generated images
- Controlling style, composition, and elements
- Working with AI-powered editing tools
Audio and Speech Processing
- Generating speech from text-based prompts
- AI-driven audio enhancement and synthesis
- Creating voice interactions with AI
Video Content Creation with AI
- Generating video clips using AI prompts
- Combining AI-generated text, images, and audio
- Editing and refining AI-created video content
Integrating Multimodal AI in Workflows
- Combining text, image, and audio outputs
- Building automated AI-driven content pipelines
- Case studies and real-world applications
Ethical Considerations and Best Practices
- AI bias and content moderation
- Privacy concerns in multimodal AI
- Ensuring responsible AI use
Summary and Next Steps
Requirements
- An understanding of AI models and their applications
- Experience with programming (Python recommended)
- Familiarity with APIs and AI-driven workflows
Audience
- AI researchers
- Multimedia creators
- Developers working with multimodal models
14 Hours
Testimonials (1)
Very well adjusted