Generative AI Course Content :
Module 1 – Introduction to Generative AI
- What is Generative AI?
- Evolution from ML to DL to Gen AI
- Key Use Cases: Text, Code, Image, Audio, Video
- Gen AI vs Traditional AI (Discriminative vs Generative)
- Transformers: Architecture & Attention Mechanism
Module 2 – Customize Gen AI
- Fine Tuning
- Fine-Tuning Techniques
- Prompt Engineering
- When to fine-tune vs prompt engineer
- Dataset Preparation
- Tokenization
- Popular LLMs and what to use when
Module 3 – Power Tools in Gen AI
- Vector Embeddings
- Retrieval-Augmented Generation (RAG)
- Hyperparameters
- Memory
- Agents
Module 4 – LLMs in Action
- Text Completion, Summarization, Translation
- Text-to-Image
- Image-to-Text
- Text-to-Speech
- Speech-to-text
Module 5 – Being Responsible with Gen AI
- Evaluating LLM Outputs
- Detecting and Reducing Hallucinations
- Bias and Fairness in Gen AI
- Toxicity and Content Filtering
- Ethical Considerations and Responsible AI Use
- Regulations and Governance
Module 6 – End-to-End Gen AI project
- Use Case Selection
- Prompt & Data Design
- LLM Integration
- Application Building
- Deployment Tools
- Evaluation & Monitoring