Your AI Career.
Built in 16 Weeks.
One continuous program, one certification — from your first line of Python to a model your team designs, builds, and ships to the cloud.
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About The Programme
One Program. One Certification.
Sixteen weeks, one continuous journey — from your first Python program to a model your team designs, builds, and deploys together. No split tracks, no separate sign-ups.
Foundations of AI & Python
Start from zero. Write real Python programs, clean and explore data, and get comfortable with the math AI actually runs on.
- 4 weeks
- Beginner — no coding experience required
- 3 labs + 2 mini projects
- AI and Python basics: variables, loops, functions, data structures
- NumPy, Pandas, data cleaning, exploratory data analysis
- Linear algebra, probability, and statistics for AI
- Intro to Machine Learning — supervised vs. unsupervised, train/test split
- Mini projects: build simple Python programs, run EDA on a sample dataset, train your first model (Linear Regression) with scikit-learn
Intermediate Machine Learning
Move from theory to models that actually predict something — train, tune, and evaluate them properly.
- 4 weeks
- Builds directly on Phase 1
- Supervised learning: regression, decision trees, random forest, KNN, evaluation metrics
- Unsupervised learning: K-Means, hierarchical clustering, PCA
- Model optimisation: cross-validation, grid search, hyperparameter tuning, pipelines
- Projects: house price prediction, disease prediction, spam detection, customer segmentation, an end-to-end ML pipeline built as a team
Advanced AI & Deep Learning
Go deep — neural networks, computer vision, NLP, and the generative AI concepts every AI role expects you to know now.
- 4 weeks
- Deep learning, computer vision, NLP, generative AI
- Deep learning basics: neural networks, backpropagation, TensorFlow/PyTorch
- Computer vision: CNNs, image classification, object detection
- NLP: tokenisation, sentiment analysis, transformers
- Generative AI & LLMs: prompt engineering, fine-tuning, AI ethics
- Projects: an image classifier (cats vs. dogs), a face mask detection model, a sentiment analysis model
Capstone Project & Career Launch
Work in a team of 3–5 to build a real AI solution from scratch, deploy it, and defend it in front of an industry panel.
- 4 weeks
- Team project (3–5 people)
- Project planning and problem definition, with mentor-allocated teams
- Model building, with weekly mentor reviews
- Model optimisation and deployment to AWS or GCP via Streamlit or Flask
- Final presentation, technical viva, and industry evaluation
Build Your Capstone in a Domain You Care About
Your capstone team picks a real-world problem to solve — not a generic assignment.
Healthcare AI
Retail Analytics
Manufacturing AI
Predictive Maintenance
Smart Campus
Computer Vision Auto.
Agriculture AI
NLP Chatbots
Finance AI
Who Should Enrol
Still in engineering or CS?
Get a head start most graduates don’t have — walk into interviews with a deployed model, not just a transcript.
Already working in IT or software?
Add AI and ML to what you already do, without starting over — you’re building on skills you already have.
Thinking about moving into AI from any working role?
This program is built for exactly that move — Python to a deployed model, in 16 weeks.
What You Will Achieve
Clear outcomes after 16 weeks.
By the end of the program, you will have:
- Written and debugged real Python programs, not toy exercises
- Cleaned, explored, and visualised a real dataset before touching a model
- Trained, tuned, and evaluated models across regression, classification, and clustering
- Built a CNN that classifies images and an NLP model that reads sentiment
- Written and tested prompts for a real LLM, and learned when fine-tuning is worth it
- Shipped a team-built model to AWS or GCP using Streamlit or Flask
- Walked out with a GitHub portfolio, a resume built around your capstone, and completed interview prep
Job roles this prepares you for:
Built the way AI teams actually work, not the way a classroom does
From week 5 onward, you’re not just watching lectures — you’re training models on real datasets, in Jupyter, the same environment working ML engineers use every day.
By week 13 you’re on a team of 3–5, building something from scratch: planning it, building it, breaking it, and shipping it to AWS or GCP — then defending it in a live demo and technical viva, the way you would in a real job.
By week 13 you’re on a team of 3–5, building something from scratch: planning it, building it, breaking it, and shipping it to AWS or GCP — then defending it in a live demo and technical viva, the way you would in a real job.
Tools
Python
Jupyter Notebook
NumPy
Pandas
Scikit-learn
TensorFlow
PyTorch
OpenCV
NLTK
Streamlit
Flask
Git & GitHub
AWS
Google Cloud
Common Questions
Frequently Asked
Do I need a coding or math background to join?
No advanced math required going in. Phase 1 covers Python and the math you need — linear algebra, probability, statistics — from scratch.
What is the weekly session schedule?
10 hours a week, split across weekdays and weekends.
Is there placement support?
Yes. The program includes career guidance, resume building, GitHub portfolio support, and interview preparation, alongside certification and placement assistance.
Do I get a certificate?
Yes — a Certificate of Completion, awarded after your capstone project, final presentation, and technical viva in week 16.
How does the team capstone project work?
You’ll work in a team of 3–5 across weeks 13–16, picking a real-world domain to solve for, and take it from idea to a deployed, demoed product with mentor reviews along the way.
Do I need a laptop or specific software?
Yes, a laptop with internet access. Every tool used — Python, Jupyter, scikit-learn, TensorFlow, PyTorch, and the rest — is free to install, and setup guides are shared before the batch starts.