Generative AI Course | Generative AI Professional Course, Training and Certification in Hyderabad,India@7993762900
Genius IT is the leading generative AI course, training and certification provider in India, helping individuals showcase their expertise. We offer specialized Generative AI Professional courses to help you excel in this growing field. Our Generative AI course in Hyderabad is an intensive program designed to equip you with the knowledge and skills required to harness the power of Generative AI. You will learn the fundamentals, advanced techniques, and practical applications of Generative AI Training in Hyderabad
Generative AI Certification Program(CBP) Exam Format:@7993762900
- Exam Format – Objective Type, Multiple Choice
- Exam Duration – 1 Hour
- No. of Questions – 40 (multiple-choice questions)
- Passing marks: 26 out of 40 (65%)
- Certificate – Within 5 business days
- Result – Immediately after the exam
- Complimentary Retake – Yes
- Closed book
Generative AI Training Course Content@7993762900
Generative AI Training Modules:
Lesson 1 – Introduction of GenAI
1. Introduction to Generative AI
2. Generative AI Applications
3. Understanding Probability and Statistics in Generative AI
4. Introduction to Generative Models
5. Deep Learning for Generative Models
6. Introduction to Generative Adversarial Networks (GANs)
7. Autoencoders
8. Transformers and Attention Mechanisms – “Attention is all you need”.
Lesson 2 – Introduction of LLM Model
1. Introduction to Large Language Models (LLMs)
2. Architecture of Large Language Models
3. Text AI LLMs (GPT-3, GPT-4, LaMDA, LLaMA, Stanford Alpaca, Google FLAN, Poe, Falcon LLM)
4. Image AI Models & Services (Midjourney, Stable Diffusion, ControlNet (SD))
5. Video AI Models (Runway – Gen 1 & 2, Kaiber, D-ID)
6. Audio AI Models (ElevenLabs)
Lesson 3 – Learning Prompt Engineering using ChatGPT
1. Introduction to Prompt Engineering
2. Introduction to ChatGPT
3. Designing a prompt – The process and workflow
4. Avoiding prompt injections using delimiters
5. Defining constraints
6. Zero-shot Prompting
7. Few-shot Prompting
8. Persona Prompting
9. Chain of Thought
10. Adversial
Lesson 4 – Customizing LLM for own data
1. Type of Customization(Fine Tunning, Embeddings,RLHF)
2. Knowledge Graphs
3. Search-deep dive
Lesson 5 – GenAi and Enterprise Architecture
1. Enterprise Architecture overview
2. Gen AI postioning within Enterprise Architecture
3. Attention Architecture
4. Transformer Architeture
5. End to End AI Model Architecture with GenAI
6. Day in life of Data Scientist
Lesson 6 – Industrialisation and demos
1. How to industrialize models and enable ModelOps
2. When and how to recalibrate,re-train,re-build models
3. Search Architecture
4. Chatbot Architecture
5. Domain specfic architectures
Lesson 7 – Advanced AI App Development using Langchain
1. What is LangChain and when should you use it?
2. The LangChain Ecosystem
3. Supported LLMs
4. Case Study: Getting started with LangChain and OpenAI
5. Prompt composition and templates
6. Using multiple LLMs (Chains)
7. Working with Data loaders – Ingesting documents
8. Working with text splitters – Chunking Data
9. Working with Chains (Conversational Retrieval QA, Retrieval QA, Summarization, API etc.)
10. Working with Memory
11. Working with Embedding
Lesson 8 – Non-Microsoft Solution
1. OpenSource LLM-Overview
2. Other LLM Models(Cohere,AI21 etc)
3. Text AI LLMs (GPT-3, GPT-4, LaMDA, LLaMA, Stanford Alpaca, Google FLAN, Poe, Falcon LLM)
4. Image AI Models & Services (Midjourney, Stable Diffusion, ControlNet (SD))
5. Video AI Models (Runway – Gen 1 & 2, Kaiber, D-ID)
6. Audio AI Models (ElevenLabs)
Lesson 9 – Responsible AI in GenAI
1. When should retrain our LLM Model
2. Impact on environment
3. Biases and other ethical Issues
4. Copyrights and ownership
5. License types for models and its implications