Certification in Generative AI
Upskill, Reskill and Become a Thorough Gen AI Professional
Earn Certification
Application Based Learning
40 hrs live online sessions
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Industry Overview
Generative AI is revolutionizing industries with its ability to create new content and solutions, enhancing business models and customer experiences. Key applications include marketing, media, manufacturing, healthcare, finance, and tech. The market, worth $10.79 billion in 2022, is expected to reach $118.06 billion by 2032, growing at a CAGR of 27.02%. Future advancements will rely on collaboration and tool integration. Responsible development and deployment are crucial for maximizing its benefits.
About the Program and Key Takeaway
This comprehensive course on Generative AI covers the foundational concepts, advanced models, and practical applications of Generative AI technologies. Participants will explore the differences between traditional AI and Generative AI, delve into various generative models including GANs, autoencoders, and transformers, and understand the architecture and applications of large language models (LLMs). The course also includes hands-on projects and practical sessions on prompt engineering, the use of vector databases, LangChain, and Retrieval-Augmented Generation (RAG). Ethical considerations, biases, and the environmental impact of AI are also discussed, along with best practices for model retraining, recalibration, and implementing guardrails.
Understanding Generative AI Models
Mastering Large Language Models (LLMs)
Prompt Engineering Techniques
Implementing Vector Databases
Utilizing LangChain for LLMs
Ethical and Practical Considerations
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Curriculum
Introduction to Generative AI
Generative AI Applications
Traditional AI Vs GenAI
Introduction to Generative Models
Introduction to Generative Adversarial Networks (GANs)
Autoencoders
Transformers and Attention Mechanisms - "Attention is all you need".
Introduction to Large Language Models (LLMs)
Architecture of Large Language Models
Text AI LLMs (GPT-3, GPT-4, LaMDA, LLaMA, Google FLAN, Falcon LLM)
Image AI Models & Services (Midjourney, Stable Diffusion)
Video AI Models (Runway - Gen 1 & 2, Kaiber, D-ID)
Audio AI Models (ElevenLabs)
Introduction to Prompt Engineering
Introduction to ChatGPT
Designing a prompt - The process and workflow
Avoiding prompt injections using delimiters
Defining constraints
Zero-shot Prompting
Few-shot Prompting
Persona Prompting
Chain of Thought
Adversial Prompting
Project: Data Analysis using ChatGPT4 & Gemini
Project: Implement ML LifeCycle using GenAI tool
Use of Vector Database in Gen AI application
Vector DB vs Graph Database
Introduction to ChromaDB
Collection and metadata creation using chromaDB
Vector DB Benefits
What is LangChain and when should you use it?
The LangChain Ecosystem
Supported LLMs
Case Study: Getting started with LangChain and OpenAI
Prompt composition and templates
Using multiple LLMs (Chains)
Working with Data loaders - Ingesting documents
Working with text splitters - Chunking Data
Working with Chains (Conversational Retrieval QA, Retrieval QA, Summarization, API etc.)
Working with Memory
Working with Embedding
Different Model Evaluation metrics like BLEU ,ROUGE
Introduction to RAG
Improve RAG ny reranking of context
Adanced RAG
Cache mechinism in RAG
Project: ChatBot implementation using RAG
when should retrain our LLM Model
impact on evironment
Biases and other ethical Issues
Copyrights and ownership
License types for models and its implications
Inteoduction to Different LLM llama2, Mistreal,Gemma etc
When and how to recalibrate,re-train,re-build models
Search Architecture
Chatbot Architecture
Domain specfic architectures
What are guard rails and why they are required
Different type of guard rails - Low code / no code, programmatic
Diffeerent type of GuradRail framework: Guardrails AI, NeMo Guardrails
Admission Process
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Apply by paying registration fees
Submit documentation for getting shortlisted
Receive coveted admission offer leter
Pay 1st Instalment to secure admission
Programme Fee Structure
Programme Fee
₹ 5,000
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