Duration
12 Weeks
Format
Blended (Live online + Labs + Projects + Community)
Target Audience
Beginners (tech-individuals with basic Python knowledge)
Certification
Microsoft – Azure AI Fundamentals (AI-900)
Module 1: Foundations of Generative AI
- Introduction to Generative AI & Deep Learning
- Generative Model Basics (VAEs & GANs)
- NLP & Transformers Fundamentals
- LLMs and APIs
Module 2: Practical Generative AI
- Prompt Engineering & Custom GPTs
- Multimodal AI (Text+Image+Audio)
- Fine-tuning & Retrieval-Augmented Generation (RAG)
- Open Source Gen AI Ecosystem
Module 3: Advanced Topics & Capstone
- Agents, Tools & AutoGPTs
- Ethics, Safety, and Alignment
- Productionizing Gen AI Apps
- Capstone Projects
Total Duration
90 Hours
Delivery Format
Online/Offline, Instructor-led or Self-paced
Materials Provided
Slides, Code Templates, Project Notebooks, Interview Guide
Module 1: Foundations of GenAI and LLMs
- Introduction to GenAI, LLMs, ML vs DL vs GenAI
- Python for AI: Data types, loops, functions, File I/O
- GitHub, Jupyter/Colab, APIs with requests
- Setting up HuggingFace, OpenAI, Google Gemini APIs
Module 2: Prompt Engineering and Evaluation
- Prompt types: Zero-shot, Few-shot, CoT, ReAct, Role prompting
- Prompt formatting: Temperature, tokens, function calling
- Prompt testing with GPT-4o, Claude, Gemini
- Scoring and refinement techniques (CoT, Self-Refine)
- Resume Rewriter using prompt templates
Module 3:Retrieval-Augmented Generation (RAG)
- Ethical AI & Responsible GenAI: hallucination risks, bias mitigation, deployment best practices
- Introduction to RAG and vector databases
- FAISS, Pinecone, Chroma: Indexing and retrieval
- Embedding generation with OpenAI and HuggingFace
- LangChain and LlamaIndex pipelines
- Document loaders, hybrid search
Module 4: LLM Fine-tuning, Evaluation & Deployment
- LoRA, QLoRA, PEFT techniques
- Fine-tuning with HuggingFace PEFT
- Quantization and inference optimization (INT8, FP16, GPTQ)
- LLM Evaluation Metrics: perplexity, BLEU, ROUGE, task-completion accuracy; open-source models overview
- Scalable deployment with Docker, Streamlit, FastAPI to cloud platforms (Render, HuggingFace Spaces)
Module 5: Multi-Agent AI Systems & Cloud Scaling
- Principles of agentic AI, ReAct, Plan-Execute workflows
- LangChain Agents, tools, memory
- AutoGen, CrewAI, LangGraph usage
- Containerizing multi-agent apps; CI/CD basics with Terraform & Kubernetes
- Debugging and multi-step task planning at scale
Module 6: Multimodal and Real-Time GenAI
- DALL-E 3, BLIP-2, Gemini Vision
- Whisper for speech-to-text
- LIP and visual Q&A systems
- Building interfaces with Gradio
- Combined chatbot with voice and visual prompts
Module 7: Domain-Specific GenAI Applications
- Marketing: Sentiment and campaign generation
- Finance: Earnings summarization, fraud explainer
- HR: Resume matcher, onboarding bot
- DSoftware Dev & Supply Chain Apps
- Healthcare and Aviation use cases
Module 8: Capstone and Career Preparation
- Capstone planning and implementation
- GitHub structuring and documentation
- LinkedIn profile and project showcases
- Resume rewriting and optimization & Technical and behavioral interview preparation
Duration
12 Weeks
Level
Intermediate
Format
Online (Blended: Live + Self-paced + Labs + Capstone)
Professional Certification
Azure AI Engineer Associate (AI-102)
Module 1: Deep Generative Modeling Fundamentals
- Neural Foundations of Generative AI
- VAEs & GANs
- Transformers & Attention
- LLMs in Practice
Module 2: Advanced LLM Applications
- Prompt Engineering & Advanced Use
- Multimodal Generative AI
- Fine-Tuning & RAG Systems
- Open-Source Model Deployment
Module 3: Agents, Safety, and Deployment
- Agents, Tools & Automation
- AI Safety & Ethics
- Productionizing Gen AI Apps
- Capstone Project
