Generative AI & Agentic AI
1 – Introduction to Generative AI
- What is Artificial Intelligence, Machine Learning, and Generative AI
- Evolution of AI technologies
- History of Generative AI (GANs to Large Language Models)
- Overview of AI-generated content (text, image, audio, video)
- Real-world applications across industries
- Ethical considerations and responsible AI
2 – Applications of Generative AI
- AI in software development
- AI in business analysis and product management
- AI in marketing, customer service, and automation
- Use cases in healthcare, finance, and education
- Demonstration of AI-generated content
3 – Generative Models Overview
- Discriminative vs Generative Models
- Overview of Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Transformer models
- Key differences and practical use cases
4 – Understanding Large Language Models (LLMs)
- What are Large Language Models
- GPT architecture basics
- Tokenization and embeddings
- How LLMs are trained
- Popular models: GPT, LLaMA, Claude, Gemini
- Limitations of LLMs (hallucinations, bias)
5 – Prompt Engineering Basics
- What is a prompt
- Structure of an effective prompt
- Zero-shot prompting
- One-shot prompting
- Few-shot prompting
- Examples of good vs bad prompts
6 – Advanced Prompt Engineering
- Role-based prompting
- Chain-of-thought prompting
- Prompt templates for analysis and coding
- Prompt optimization techniques
- AI prompting for business tasks
7 – Hands-on with ChatGPT
- Getting started with ChatGPT
- Prompt templates for:
- Coding
- Summarization
- Brainstorming
- Documentation
- Productivity hacks using GPT
- Using AI for requirement documentation
8 – Data Preparation for AI
- Importance of data in AI systems
- Data transformation concepts
- Data scaling and encoding
- Handling categorical data
- Date and time operations
- Data cleaning fundamentals
9 – Exploratory Data Analysis (EDA)
- Understanding datasets
- Data merging and joining
- Grouping and aggregation
- Identifying trends and patterns
- Case study: Superstore Sales / IPL dataset
10 – Statistics & Probability for AI
- Descriptive statistics
- Mean
- Median
- Mode
- Standard deviation
- Introduction to probability theory
- Probability distributions
- Normal distribution
- Binomial distribution
- Poisson distribution
11 – Data Visualization
- Importance of data visualization
- Matplotlib visualization techniques
- Line chart
- Bar chart
- Pie chart
- Scatter plot
- Histogram
- Seaborn visualizations
- Boxplot
- Heatmap
- Pairplot
- Visual storytelling with datasets
12 – Introduction to AI Agents
- What are AI agents
- Components of an AI agent
- Reasoning
- Memory
- Tools
- Difference between traditional automation and AI agents
- Examples of AI agents in business
13 – Agentic AI Concepts
- What is Agentic AI
- Autonomous decision-making systems
- Multi-agent collaboration
- Agent orchestration
- Real-world examples of Agentic AI
14 – Building AI Agents with CrewAI
- Overview of CrewAI framework
- Roles of agents in CrewAI
- Task orchestration
- Creating multi-agent workflows
- Example use case: research and reporting agent
15 – No-Code AI Automation with n8n
- Introduction to n8n automation platform
- Workflow automation concepts
- Drag-and-drop automation pipelines
- Integrating APIs and AI services
- Building automated AI workflows
16 – AI + RPA Integration using UiPath
- Overview of Robotic Process Automation
- AI integration with UiPath
- UiPath AI Center overview
- Automating document processing with AI
- Business automation use cases
17 – Retrieval Augmented Generation (RAG)
- What is Retrieval Augmented Generation
- Why RAG is used in enterprise AI
- Knowledge base integration
- Vector databases and embeddings
- Building AI assistants using RAG
18 – Fine-Tuning Concepts
- What is fine-tuning
- Difference between fine-tuning and prompt tuning
- When fine-tuning is required
- Introduction to model customization
- Fine-tuning use cases in enterprise AI
19 – Cloud AI Platforms
- Overview of AI infrastructure
- AWS AI services
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- AWS Bedrock
- Amazon SageMaker
- Google Cloud AI services
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- Vertex AI
- Generative AI Studio
- Deploying AI models on cloud
20 – Capstone Project: Building an Agentic AI Solution
- Designing an end-to-end AI solution
- Building an AI agent workflow
- Integrating LLMs with automation tools
- Implementing AI agents using CrewAI / n8n
- Demonstration and presentation of projects