Ages 18–20 · Applied · Foundation Programme v2
AI & Machine Learning Foundation Programme
A 12-week hybrid programme — self-study + a weekly online live class + a 2-week face-to-face intensive — taking university-age learners from foundations to a deployed capstone, evaluated by external industry mentors.
Eight cross-cutting themes, every week
Rather than treating GenAI, MLOps, ethics and portfolio as standalone modules, v2 weaves all eight throughout. Every weekly plan is tagged with the themes it advances.
Generative AI & LLMs
Prompt engineering, LLM classification, RAG, LangChain, agentic AI woven across 5 weeks.
MLOps & Deployment
MLflow from W4, monitoring W8, full MLOps in W11 — Docker, FastAPI, cloud, CI/CD.
Portfolio Building
Public GitHub + blog from W1, every case study published, audits at W8 and W12.
Professional Skills
Weekly 30-min module: READMEs, stakeholder translation, presentations, interview prep.
Ethics & Responsible AI
Weekly 15-min case study, fairness audits on every classifier, full day on EU AI Act.
Data Quality Depth
Bias detection in datasets, DVC versioning, data leakage taxonomy.
Human-AI Collaboration
Copilot/Claude Code from W1, verify-AI-output checkpoints in every lab.
Continuous Learning
Habit framework W1, mentor matching W10, 90-day post-programme roadmap W12.
Four phases, one continuous arc
Foundations to Core ML to a 2-week in-person Deep Dive to Application & Capstone — each phase ends with assessment, portfolio audit and published case studies.
Foundations
AI landscape, Python, maths, data, ML pipeline, first MLOps tools.
Core ML
Regression, classification with LLMs, unsupervised learning, evaluation and monitoring.
Deep Dive Intensive
Neural nets, NLP, RAG, LangChain, agentic AI, capstone kickoff with industry mentor.
Application & Capstone
MLOps deep dive, deployed LLM application, capstone presentation, 90-day roadmap.
The 12-week plan
Open any week to see topics, the weekly deliverable, the ethics moment and the assessment. Coloured left edge marks the phase.
13–15 hours a week
Six-stage learning cycle
- 1
Concept
Introduce the idea. Pre-class reading, video, real-world motivation.
- 2
Understand
Build mental model. Live walkthrough, visuals, Q&A, worked examples.
- 3
Validate
Check understanding. Quick quiz, peer discussion, explain-back.
- 4
Practice
Apply hands-on. Guided coding labs, Jupyter/Colab, structured exercises.
- 5
Case Study
Solve real problems. Published to portfolio with written analysis.
- 6
Assessment
Prove mastery. Quiz, peer review, reflection journal.
The stack you'll actually use
Modern, employer-relevant tooling — including AI coding assistants from day one with explicit verify-AI-output checkpoints in every lab.
Python & data
MLOps
GenAI
AI coding & publishing
Industry partner
Built on the same analytics stack hiring teams use.
Applied is co-delivered with AIV Hub — an AWS & Azure marketplace product for Active Intelligence Visualization. Learners ship to a real production surface from Week 4.
Real-world dashboards from Week 4
Phase 1 deliverables ship to AIV Hub — not toy datasets. Learners build analytics dashboards on a live product surface.
Capstone deployment surface
Week 11–12 capstones deploy as AIV-powered dashboards, evaluated by external AIV Hub product mentors.
Cloud-marketplace ready
Experience an AWS & Azure marketplace product end-to-end — the same stack hiring teams already use.
How learners are graded
70% to pass. Capstone is evaluated by external industry mentors against the v2 rubric.
90-day post-programme roadmap
The programme doesn't end at graduation — every learner leaves with a structured 90-day plan, a curated reading list and alumni community access.
Days 1–30
Deepen one specialisation — NLP/LLMs, CV, MLOps, Research or AI Product
- •One new portfolio project in your specialisation
- •Read 5 foundational papers in that area
- •One open-source pull request
- •Weekly blog post
Days 31–60
Build and deploy — integrate MLOps, GenAI and your specialisation
- •Production-grade project publicly deployed
- •Monitoring dashboard for the deployed model
- •Architecture decisions blog post
- •Attend one AI meetup
Days 61–90
Apply and network — convert portfolio into opportunities
- •Polish LinkedIn with project links
- •Apply to 10+ roles or internships
- •Present project at meetup or student society
- •Mentor a peer — teaching cements learning
Where it leads
Pathway to CS / AI degrees
Foundations for Year 1 undergrad
ACM / IEEE CS2023
AI / ML knowledge areas
Employer-relevant stack
Python · scikit-learn · TF/Keras · FastAPI · MLOps
Common questions
Run the Applied programme with us
We co-deliver with universities and industry partners across emerging economies. Talk to us about cohorts, pricing and the F2F intensive.