Course Information
Duration
12 Weeks (10-15 hours/week)
Primary Language
Python 3.10+
Level
Intermediate to Advanced
Format
Self-paced with hands-on labs
Note: While Python-focused, Week 11 covers multi-language integration patterns (Java/C#) for enterprise scenarios.
Prerequisites
Required
- 5+ years of IT/software development experience
- Strong Python programming skills
- Understanding of REST APIs and web services
- Basic Git and command line proficiency
Recommended
- Experience with Docker
- Basic understanding of machine learning concepts
- Familiarity with cloud platforms (AWS/Azure/GCP)
Course Curriculum
WEEK 0: Environment Setup & Python Refresher (Optional)
- Professional Python development environment setup
- Python (NumPy, Pandas, async/await)
- AI coding assistants configuration (GitHub Copilot, Cursor, Aider)
- Git workflows and modern Python practices
- Python 3.10+ installation, virtual environments (venv/conda)
- Jupyter Lab, VS Code with Python extensions
- NumPy, Pandas essentials for data manipulation
- API fundamentals: REST, JSON, authentication
- Type hints and modern Python features
- Async/await for concurrent programming
Deliverable: Configured development environment
Hands-on Labs
- Lab 0.1: Environment setup checklist - Install Python, pip, virtualenv, Configure VS Code with extensions, Set up pre-commit hooks
- Lab 0.2: Build a CLI weather app using a public API - Use requests library, Environment variable management, Error handling and retries
- Lab 0.3: Data manipulation exercise with Pandas - Load CSV, clean data, Basic transformations, Export results
WEEK 1: AI/ML Foundations for GenAI
- AI vs ML vs Deep Learning vs GenAI taxonomy
- Neural networks and transfer learning basics
- Python ML ecosystem overview
- Building REST APIs with FastAPI
- Transfer learning and pre-trained models
- Introduction to transformers (high-level)
Deliverable: Build and deploy a simple ML classifier REST API with FastAPI
WEEK 2: Language Models & Transformer Architecture
- Transformer architecture deep dive
- Tokenization and embeddings
- BERT, GPT, T5 evolution
- Hugging Face ecosystem mastery
- NLP pipeline: tokenization, stemming, lemmatization
- Word embeddings: Word2Vec, GloVe, contextual embeddings
- Self-attention mechanism, Multi-head attention
- Positional encoding, Encoder-decoder structure
- Hugging Face Hub and model cards
Deliverable: Document Q&A system with BERT
Mini Project 1: Custom Text Classification API with Docker & CI/CD
WEEK 3: Generative AI & Large Language Models
- LLM landscape (GPT, Claude, LLaMA, Mistral)
- Working with multiple LLM APIs
- Token management and streaming
- Structured output generation (JSON mode)
- Cost optimization strategies
Deliverable: Multi-provider LLM comparison dashboard (Streamlit) with cost tracking
WEEK 4: Prompt Engineering & LLM Optimization
- Advanced prompting techniques (few-shot, chain-of-thought, ReAct)
- Prompt templating and versioning
- LLM evaluation metrics
- Content safety and guardrails
- Bias detection and mitigation
- Zero-shot, few-shot, chain-of-thought prompting
- Role-based prompting, system messages
- XML/JSON structured prompts, ReAct prompting
- Temperature, top_p, frequency_penalty tuning
- Hallucination detection strategies
Deliverable: Prompt engineering toolkit with automated evaluation
Mini Project 2: Intelligent Document Processor with CI/CD Pipeline
WEEK 5: Vector Databases & Embeddings
- Embedding models (OpenAI, Sentence Transformers)
- Vector database architectures (FAISS, ChromaDB, Qdrant, Pinecone)
- Distance metrics and indexing strategies
- Advanced chunking strategies
- Metadata filtering and hybrid search
Hands-on Labs
- Lab 5.1: Embedding comparison
- Lab 5.2: Build FAISS index from scratch
- Lab 5.3: ChromaDB with persistence
- Lab 5.4: Metadata filtering
- Lab 5.5: Chunking strategy comparison
- Lab 5.6: Hybrid search implementation
Deliverable: Vector search engine with multiple embedding models, 3+ vector DB implementations, advanced chunking, and performance benchmarks
WEEK 6: RAG (Retrieval-Augmented Generation) Systems
- RAG architecture patterns
- Multi-format document parsing (PDF, DOCX, HTML, Markdown)
- Query transformation and expansion
- Re-ranking with cross-encoders
- Context compression techniques
- RAG evaluation and optimization
Deliverable: Enterprise RAG system with evaluation suite
Mini Project 3: Multi-Tenant Knowledge Base with Advanced RAG
WEEK 7: Agentic AI Fundamentals
- Agentic AI vs traditional chatbots
- Agent anatomy: reasoning, planning, memory, tools
- ReAct (Reasoning + Acting) paradigm
- Tool/function calling mechanisms
- LangChain, LangGraph, SpringAI framework
- Error handling and fallback strategies
Deliverable: Multi-tool agent solving 3+ step tasks
WEEK 8: Advanced Agentic Systems & Multi-Agent Workflows
- Multi-agent architectures (sequential, hierarchical, collaborative)
- Agent communication protocols
- Memory systems (short-term, long-term, working memory)
- Human-in-the-loop integration
- Visual agent builders (Flowise, LangFlow)
- Agent orchestration with LangGraph
Deliverable: Multi-agent research system with persistent memory
WEEK 9: Model Context Protocol (MCP) Integration
- MCP architecture and protocol specification
- Building custom MCP servers in Python
- MCP client integration patterns
- Enterprise system integration (databases, APIs, cloud services)
- Security and authentication for MCP
- Production deployment of MCP servers
Deliverable: Custom MCP server suite with agent integration
Mini Project 4: Autonomous Business Process Agent with MCP
WEEK 10: AI-Assisted Development & IDE Mastery
- AI coding assistants deep dive (GitHub Copilot, Cursor, Aider)
- GitHub Copilot CLI for DevOps automation
- IDE optimization for AI development (VS Code, PyCharm)
- Live coding best practices and pair programming with AI
- AI-assisted code review in CI/CD
- Debugging with AI assistance
- Future trends in AI-augmented development
Deliverable: AI-assisted development workflow with documented examples
WEEK 11: LLMOps, Production Deployment & Multi-Language Integration
- LLMOps lifecycle and best practices
- Deployment patterns (Docker, Kubernetes, serverless)
- Monitoring and observability (Prometheus, Grafana, LangSmith)
- Semantic caching and cost optimization
- Security, compliance, and governance
- Multi-language integration: Java and C# patterns for enterprise
Deliverable: Production-deployed LLM application with full monitoring
WEEK 12: Advanced Topics & Future-Ready Skills
- Fine-tuning LLMs (LoRA, QLoRA)
- Model quantization and optimization
- Language Server Protocol (LSP) for AI tooling and code intelligence
- Abstract Syntax Trees (AST) - ANTLR, Tree-sitter for code analysis
- Knowledge Graphs for enhanced RAG (Neo4j, RDF, NetworkX)
- Graph RAG and hybrid retrieval systems
- Code understanding and generation using AST parsing
- Semantic code search and analysis
- Emerging trends: multimodal AI, agent-based development, compound AI systems
Deliverable: Advanced implementation (Fine-tuned model OR Knowledge Graph RAG OR AST-based tool)
WEEKS 11-12: Capstone Project
Choose one of four project tracks:
- Option A: Multi-Language Enterprise RAG Platform
- Option B: Autonomous Business Process Agent System
- Option C: AI Development Platform with Code Analysis (AST/LSP)
- Option D: Custom Use Case
Requirements
- Production-ready implementation
- Complete CI/CD pipeline with quality gates
- Monitoring and cost tracking
- Comprehensive documentation
- Live demonstration and presentation
- Architecture and design documentation
Evaluation Criteria
Technical implementation (30%), GenAI/Agentic features (20%), Production readiness (20%), CI/CD (10%), Documentation (10%), Presentation (5%), Innovation (5%)
4 Mini Projects Throughout Course
1
Text Classification API
Weeks 1-2
2
Document Processor
Weeks 3-4
3
Knowledge Base with RAG
Weeks 5-6
4
Business Process Agent
Weeks 7-9
Technology Stack
Core Technologies
- Language: Python 3.10+
- AI/ML: Hugging Face Transformers, Sentence-Transformers, OpenAI/Anthropic SDKs
- Frameworks: LangChain, LangGraph, CrewAI, AutoGen
- Vector DBs: FAISS, ChromaDB, Qdrant, Pinecone
- Databases: PostgreSQL, Redis, Neo4j
Development Tools
- IDEs: VS Code, Cursor, PyCharm, Jupyter Lab
- AI Assistants: GitHub Copilot, Aider, Continue.dev
- API Development: FastAPI, Pydantic
- Testing: pytest, Locust
DevOps & Deployment
- Containers: Docker, Docker Compose, Kubernetes
- CI/CD: GitHub Actions
- Monitoring: Prometheus, Grafana, LangSmith, LangFuse
- Cloud: AWS/Azure/GCP (optional)
Advanced Tools
- Code Analysis: ANTLR, Tree-sitter, Rope, Jedi
- LSP: Python Language Server, Pylance
- Knowledge Graphs: Neo4j, NetworkX, RDFLib
- Documentation: Sphinx, MkDocs
Learning Outcomes
Upon completion, you will be able to:
Build production-ready GenAI applications in Python
Design and implement advanced RAG systems with optimization
Develop autonomous agentic workflows with multi-agent collaboration
Create and integrate MCP servers for enterprise systems
Deploy LLM applications with full CI/CD pipelines
Use AI coding assistants to increase productivity 10x
Master IDE workflows for AI development
Fine-tune and optimize LLMs for specific domains
Implement monitoring and cost optimization at scale
Work with LSP and AST for code intelligence and analysis
Build knowledge graph-enhanced RAG systems
Integrate multi-language systems (Python/Java/C#) in enterprise
Course Highlights
70%
Hands-On - 4 mini projects + 1 capstone project
🚀
Production-Focused - Real deployment, monitoring, CI/CD
🐍
Python-First - Deep dive into Python AI ecosystem
🌐
Multi-Language Aware - Enterprise integration patterns
🔮
Future-Ready Skills - MCP, LSP, AST, Knowledge Graphs
🤖
AI-Assisted Learning - Master AI coding tools and IDE workflows
🎯
Industry-Aligned - Based on IIT curricula + current market needs
📁
Portfolio Building - GitHub projects with comprehensive documentation
Additional Resources Included
Access to premium AI tools and APIs
Curated reading lists and documentation
Job search and interview preparation
Certificate of completion
Time Commitment
- Lectures & Tutorials: 2-3 hours/week
- Hands-On Labs: 6-8 hours/week
- Reading/Research: 2-3 hours/week
- Projects: 10-15 hours (every 2-3 weeks)
- Total: 10-20 hours/week depending on pace