Generative AI with IBM Projects Curriculum
Unit -1 Introduction to Generative AI
- Generative AI Overview: Definition, evolution, and importance.
- Understanding the differences between Generative AI and traditional AI approaches. Use cases in text, image, speech, and code generation
- Large Language Models (LLMs): Transformers, Attention Mechanism, and IBM Granite.
- Hands-on Activity: Exploring IBM Granite to generate text outputs.
Unit - 2 Generative AI Development Tools
- Watsonx AI Suite: Overview and setup.
- Streamlit for AI Applications: Environment setup and integration with AI models.
- Hands-on Activity: Building a web app with IBM Granite for real-time responses.
Unit - 3 Applications of Generative AI
- Text Analysis: Summarization, sentiment analysis, and classification.
- AI-Powered Image Generation: Applications in marketing and design.
- Case Studies: Examples in healthcare, finance, and ecommerce.
- Hands-on Activity: Building text summarization and image generation tools.
Unit - 4 Advanced Techniques in Generative AI
- Fine-Tuning LLMs: Dataset preparation and best practices
- Building Multi-functional Applications: Combining textto-image generation, summarization, and chatbots.
- Hands-on Activity: Fine-tuning IBM Granite and creating multifunctional AI apps.
Unit - 5 Optimization, Ethics & Real World Usage
- Performance Optimization: Techniques and production challenges.
- Ethical AI: Fairness, bias mitigation, and regulations.
- Career Trends: Applications across industries.
- Hands-on Activity: Finalizing and optimizing an AI application.