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PYTHON

Python Basics for Artificial Intelligence

Fundamentals, data structures, text processing, files, debugging, modules.

Chapter 1: Python Basics for Artificial Intelligence

  • Python Fundamentals
  • Control Flow (Decision Making & Loops)
  • Core Data Structures
  • Python Built-in Functions (AI-friendly)
  • Functions & Modular Code
  • Strings & Text Processing (NLP Foundation)
  • File Handling (Data Ingestion)
  • Error Handling & Debugging
  • Object-Oriented Programming
  • Python Modules & Packages
GENERATIVE AI

LLMs • Transformers • Prompting • RAG • Fine-tuning

Learn how GenAI works and how to use it responsibly in real projects.

Chapter 2: Foundations of Generative AI

  • What is Generative AI?
  • Generative vs Predictive AI
  • Generative AI vs Traditional ML
  • Use cases across industries

Chapter 3: Large Language Models (LLMs)

  • What is an LLM?
  • Tokens, context window
  • Pre-training vs fine-tuning
  • Instruction tuning & alignment

Chapter 4: Transformer Architecture (Conceptual)

  • Attention mechanism
  • Self-attention vs cross-attention
  • Encoder vs decoder models
  • Why transformers scale well

Chapter 4: Prompt Engineering

  • Prompt structure
  • Zero-shot, one-shot, few-shot prompting
  • Role prompting
  • Prompt optimization strategies

Chapter 5: Text Generation & Understanding

  • Text completion
  • Summarization
  • Translation
  • Question answering
  • Reasoning & chain-of-thought (conceptual)

Chapter 6: Embeddings & Vector Representations

  • What are embeddings?
  • Semantic similarity
  • Vector databases (conceptual)
  • Use cases: search, clustering, RAG

Chapter 7: Retrieval-Augmented Generation (RAG)

  • Why RAG is needed
  • Document ingestion
  • Chunking strategies
  • Retrieval + generation flow
  • RAG limitations

Chapter 8: Fine-Tuning & Customization

  • When to fine-tune vs prompt
  • Parameter-efficient fine-tuning (LoRA, adapters)
  • Domain adaptation
  • Evaluation of fine-tuned models

Chapter 9: Evaluation & Risks in Generative AI

  • Hallucinations
  • Bias & fairness
  • Safety & alignment
  • Output evaluation techniques
AGENTIC AI

Agents • Tools • Memory • Planning • Multi-agent

Design agents that can reason, act, and collaborate across systems.

Chapter 10: Introduction to Agentic AI

  • What is an AI agent?
  • Agentic AI vs Generative AI
  • Autonomous vs assisted agents
  • Real-world agent examples

Chapter 11: Core Components of an AI Agent

  • Agent (LLM brain)
  • Tools
  • Memory
  • Environment
  • Goals & constraints

Chapter 12: Agent Architectures

  • Single-agent systems
  • Multi-agent systems
  • Planner–executor pattern
  • ReAct (Reason + Act) pattern

Chapter 13: Tool Use & Function Calling

  • What is a tool?
  • Tool schemas
  • API calling
  • Code execution tools
  • External system integration

Chapter 14: Agent Memory

  • Short-term memory (context)
  • Long-term memory
  • Vector-based memory
  • Memory retrieval strategies

Chapter 15: Planning & Decision Making

  • Task decomposition
  • Planning vs execution
  • Dynamic replanning
  • Goal-oriented behavior

Chapter 16: Multi-Agent Collaboration

  • Agent roles
  • Agent communication
  • Coordination strategies
  • Conflict resolution
  • Pydantic AI framework
  • Microsoft Autogen framework
  • CreW AI framework
  • LangGraph framework

Chapter 16: MCP - Model Context Protocol

  • Understanding MCP
  • Build MCP Server

Chapter 17: LLM Deployment

  • Launching LLMs on AWS EC2 instance

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