Duration 1 day – 8 hrs
Overview
This training course provides a comprehensive introduction to generative AI and prompt engineering. Participants will learn the principles, techniques, and tools required to effectively utilize generative models like GPT-3 and GPT-4. The course covers crafting effective prompts, fine-tuning models, and applying generative AI to solve real-world problems across various domains.
Objectives
- Understand the fundamentals of generative AI and prompt engineering.
- Learn how to create effective prompts to elicit desired responses from generative models.
- Explore the architecture and functioning of models like GPT-3 and GPT-4.
- Gain hands-on experience in fine-tuning generative models.
- Apply generative AI to solve practical problems in various domains.
Audience
- AI Researchers and Practitioners
- Data Scientists
- Machine Learning Engineers
- Software Developers
- Business Analysts
- Product Managers
- Technology Enthusiasts
Prerequisites
- Basic understanding of machine learning and artificial intelligence concepts.
- Familiarity with programming languages such as Python.
- Basic knowledge of natural language processing (NLP) is helpful but not required.
Course Content
Module 1: Generative AI Foundation
- GenAI course Overview
- Define GenAI
- GenAI Demo
- Lab: Generate-Videos-in-single-prompt
- Applications of GenAI
- Technologies behind GenAI
- Part1-Ethics and Legal in AI
- Part2-Ethics and Legal in AI
- Lab: Generate ppt in 30 secs
- Managing GenAI Projects
- Security in GenAI
- Future of AI
- Lab: Generate AI voice in single prompt
- GenAI in Finance
- GenAI in Sales and Marketing
- GenAI in HR Management
- Lab: Generate AI avatar videos
- GenAI in Healthcare
Module 2: Prompt Engineering
- Define Prompt Engineering
- Prompting Technique
- Behind the scenes Prompt to output
- Lab: Prompt with Chatgpt 3.5
- Lab: Prompt with Chatgpt 4.0
- Lab1-Prompt with Anthropic Claude
- Lab: Prompt with Google Gemini
Module 3: Natural Language Processing
- Introduction to NLP
- Applications of NLP
- Evolution of NLP
- Challenges in NLP
- NLP tasks
- NLP Pipeline
- NLP tools and Libraries
- Lab 1: Email spam filtering
- Lab 2: Text summarization
- Lab 3: NLP-data-pre-processing
