Course Objective:

To equip learners with the skills to build scalable, real-time full stack applications with integrated AI capabilities — either via API or by developing Python microservices. The hybrid track empowers learners to choose between fast AI integration or deeper ML control.

Unique Selling Propositions (USP):

• Beyond CRUD: Real-time, scalable systems
• Dual AI Track: JavaScript-based API integration and Python-based ML microservices
• DevOps-enabled deployment: Docker, GitHub Actions, Railway/Render
• Capstone projects solving real-world problems relevant to India and UAE

Course Breakdown (Weekwise)

Week 1: Modern Full Stack Foundations

• Architecture overview: Monoliths vs Microservices
• Stack setup: Node.js, Express, PostgreSQL, React (Vite)
• REST API design, Postman, database models

Week 2: Real-Time & Event-Driven Apps

• WebSockets & Socket.IO
• Redis pub/sub basics
• Real-time notifications and collaboration features

Week 3: Microservices & Scalable Backends

• Microservice architecture concepts
• Messaging queues (RabbitMQ/Kafka)
• GraphQL basics
• API gateway & rate limiting

Week 4: Frontend That Scales

• Scalable project structure in React
• State management (Zustand/Redux)
• Route guards, dynamic rendering
• Responsive & accessible UI with Tailwind

Week 5: AI Integration (Parallel Tracks)

Track A: AI Integration via APIs (JavaScript)

• GPT-4 & Whisper API (OpenAI)
• Hugging Face/Replicate APIs
• Use cases: AI chatbot, voice-to-text, sentiment analysis

Track B: Python-Powered AI Microservices

• Setting up FastAPI with Docker
• Using pretrained models (transformers, scikit-learn)
• Building and exposing inference endpoints
• Calling Python ML service from Node.js

Week 6: DevOps for Developers

• Docker for full stack apps
• GitHub Actions for CI/CD
• Deployment on Railway/Render
• Monitoring & observability tools (Sentry, Prometheus)

Week 7–8: Capstone Projects

• Option 1: Smart Resume Analyzer (JS + Python ML)
• Option 2: AI Content Assistant with GPT & Whisper
• Option 3: Vision Bug Tracker (frontend + Python CV backend)
• Demo day presentation

Tech Stack Summary

• Frontend: React (Vite), Tailwind CSS, Zustand/Redux
• Backend (Core App): Node.js, Express.js, PostgreSQL, Redis
• AI (API): OpenAI, Whisper, Hugging Face, Replicate
• AI (Custom ML): Python, FastAPI, scikit-learn, Hugging Face Transformers
• DevOps: Docker, GitHub Actions, Railway/Render

Learning Outcomes:

• Build real-time apps with modern full stack tools
• Integrate AI features using APIs and custom ML models
• Apply microservices, Docker, and CI/CD to real projects
• Create resume-ready capstone projects

Certification:

Certified Full Stack Developer – AI-Ready and Scalable Systems (Hybrid Track)

Optional Add-on:

Advanced AI Micro-Series (Post-course)

• Fine-tuning models
• LangChain-powered bots
• ML pipelines with Airflow/Kubeflow