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Python for AI-Driven Automation
& Business Data Science
Python for AI-Driven Automation
& Business Data Science

Modules

  • Module 0 — Onboarding
  • Module 1 — Foundations
  • Module 2 — Data Science
  • Module 3 — Real-world I/O
  • Module 4 — Web Scraping
  • Module 5 — Machine Learning
  • Module 6 — Deep Learning with PyTorch
  • Module 7 — Industry Applications
  • Module 8 — AI Engineering
  • Module 9 — Building AI POCs
  • Module 10 — Agents, Tools & MCP
  • Module 11 — NLP (Text Analytics)
  • Module 12 — DeepTab (optional)
  • Module 13 — Production
  • Module 14 — CI/CD, Docker & Deployment
    • Architecture — the complete request path
    • Docker
    • Docker Compose
    • Container Registries — The Warehouse for Your Images
    • GitHub Actions — Your Automated Factory
    • DNS — The Internet Phone Book
    • Reverse Proxies — The Receptionist
    • HTTPS — Locking the Front Door
    • Production Deployment
    • Monitoring & Observability
    • Troubleshooting Field Guide
    • Tutorial — Build & Ship the Example App End-to-End
    • Exercises — The Capstone Bank
  • Module 15 — Capstones
  • Module 16 — Business AI
  • Module 17 — Django for AI Web Apps (optional)
    • Why Django?
    • Project anatomy
    • Models & the admin
    • Views, templates & forms
    • Auth & the history page
    • Serving a model
    • Testing & deployment
    • Exercises
  • Module 18 — Compound AI Evaluation with CAFE (optional)
  • Module 19 — Containers & Docker (from first principles)
    • Why containers
    • Images & layers — the OCI image format from the inside
    • Container internals — what actually happens on docker run
    • Dockerfiles for data science & AI workloads
    • Volumes & networking — where data lives and how containers talk
    • The ecosystem — beyond docker
    • Exercises — Container Fundamentals Bank

Course-wide

  • 🏎️ Fast Track
  • 🧪 Module Quizzes
  • Sample datasets
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