Generative AI Course (Online)

2 Months Course: $500

1. Introduction
  • Introduction to Artificial Intelligence
  • What is Generative AI?
  • AI vs ML vs Deep Learning vs GenAI
  • How Generative AI Works
  • Real-World Applications of Generative AI
  • Generative AI Tools & Ecosystem
  • Career Opportunities in Generative AI
2. Large Language Models (LLMs)
  • What is an LLM?
  • How LLMs Work
  • LLM Training & Inference
  • What are Tokens?
  • What is Context Window?
  • Parameters & Model Size
  • Popular LLMs — Gemini, GPT & Claude
3. LLM Parameters & Prompt Engineering
  • Introduction to Prompt Engineering System, User & Assistant Prompts
  • What is Temperature?
  • What is Top-P?
  • Zero-Shot & Few-Shot Prompting Role, Context & Instruction Prompting Structured & Effective Prompts
4. Google Gemini & Gemini API
  • Introduction to Google Gemini
  • Gemini Models & Capabilities
  • Google AI Studio
  • Creating & Managing Gemini API Keys
  • Connecting Gemini API with Python
  • Text & Multimodal Capabilities
  • Working with Gemini API Responses
5. Embeddings & Semantic Search
  • What are Embeddings?
  • How Text is Converted into Vectors
  • Understanding Vector Representations
  • Semantic Similarity
  • Cosine Similarity
  • Keyword Search vs Semantic Search
  • Introduction to Embedding Models
6. Vector Databases
  • What is a Vector Database?
  • How Vector Databases Work
  • Vectors & Metadata
  • Similarity Search
  • Introduction to ChromaDB
  • Introduction to Pinecone
  • Storing & Retrieving Embeddings
7. RAG — Retrieval-Augmented Generation
  • What is RAG?
  • Why Do We Need RAG?
  • RAG Architecture
  • Document Loading & Processing
  • Text Chunking & Chunk Overlap
  • Embeddings & Vector Storage
  • Retrieval & Context Generation
8. Building RAG Systems
  • Complete RAG Pipeline
  • Working with PDF Documents
  • Document Chunking Strategies
  • Generating & Storing Embeddings
  • Vector Database Integration
  • Retrieving Relevant Context
  • Connecting Retrieved Context with an LLM
9. LangChain Fundamentals
  • Introduction to LangChain
  • LangChain Architecture
  • Models & Prompt Templates
  • Chains
  • Document Loaders
  • Retrievers
  • Building RAG Applications with LangChain
10. Function Calling & AI Agents
  • What is Function / Tool Calling?
  • How LLMs Use External Tools
  • Creating Custom Tools
  • What are AI Agents?
  • Chatbot vs AI Agent
  • Agent + Tools + LLM
  • Introduction to Agent Workflows
11. Live Projects
  • Yes
12. Freelancing & Interview Guidance
  • How to work as freelancer
  • Guidance for the interviews
13. Class Hour
  • 2 Hours/Class for 3 days in a week
14. Certificate
  • Yes

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