From Deep Learning to a Deployed Capstone
Neural networks, computer vision, NLP, and generative AI — then a team project you actually ship, deployed to the cloud and demoed live.
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About The Programme
Two Courses. Your Choice.
This is Part 2 — it stands on its own and is job-ready by itself. Already done AI & ML Foundations? This is your natural next step. Already comfortable with Python, statistics, and classical ML on your own? You can start right here.
Advanced AI & Deep Learning
Beyond classical ML — neural networks, computer vision, language, and generative AI.
- 1 Month
- Intermediate
- 4 hands-on labs, 1 mini project
- Deep Learning Basics
- Computer Vision
- Natural Language Processing (NLP)
- Generative AI & LLM Concepts
Capstone Project
Teams of 3–5 build a real AI solution from scratch — planning it, building it, deploying it, and presenting it live.
- 1 Month
- Intermediate-to-advanced
- 1 capstone project
- Domains to Explore
- Project Planning & Problem Definition
- Model Development
- Model Optimization & Deployment
- Final Presentation & Demo Day
Who Should Enrol
AI & ML Foundations graduates continuing the pathway.
Anyone who already knows Python, statistics, and classical ML — and wants deep learning, deployment, and team capstone experience specifically.
What You Will Achieve
Outcomes
- You'll build and evaluate a working CNN model and an NLP model yourself.
- You'll get hands-on prompt engineering experience.
- You'll walk away with a deployed, demoed capstone project — a portfolio piece built the way real teams actually ship AI.
How You're Evaluated
- Assignments — 20%
- Mini Projects — 20%
- Internal Hackathon — 10%
- Capstone Project — 40% (the team capstone, deployed to the cloud)
- Final Viva — 10% (technical viva plus industry evaluation on Demo Day)
- Career roles you'll be ready for: Machine Learning Engineer, AI Engineer, Deep Learning Engineer (Junior)
Shipped the way real AI teams ship it, not the way a classroom does
Weeks 1–4 are hands-on from day one: deep learning, computer vision, NLP, and generative AI, built through labs and a mini project, not just watched in lectures. From week 5, you’re on a team of 3–5, building the capstone from scratch — planning it, building it, breaking it, fixing it. By Week 8, it’s live: deployed behind a real Streamlit or Flask interface on AWS or GCP, not a script running on your laptop. Then you defend it twice — a technical viva, and a live demo in front of industry evaluators on Demo Day.
Tools
TensorFlow
PyTorch
OpenCV
spaCy
Streamlit
Flask
AWS
Google Cloud
FAQs
Yes, if you’re already comfortable with Python, statistics, and classical ML.
3 to 5 people.
Yes — a standalone certificate for this course alone.
Your capstone goes live behind a real interface (Streamlit or Flask) on AWS or GCP — not just a script that runs locally.