Developer preview
Pick a course, start learning
Open any part directly — no account needed. Great for exploring before you enrol.
All courses
Introduction to AI
A full beginner pathway: what AI is, how it learns, safe prompting, everyday tools, misinformation, local context, habits, and a personal learning plan — with interactive practice throughout.
Open part 1
Corporate AI Course
Practical corporate AI: everyday tools (copilots, assistants, automation), improving management and team workflows, document and meeting productivity, internal knowledge search, customer-service bots, integrations with CRM/ERP, safe adoption, vendor choice, and a workplace AI rollout capstone.
Open part 1
AI Literacy & Skills Programme
A complete learner pathway from AI concepts through prompting, ML intuition, generative ethics, governance, automation, research, collaboration, portfolios, careers, community projects, and a personal showcase capstone.
Open part 1
High School AI Architect Course
The flagship teen programme: tokens, data centers, economics, prompting, bias, multimodal AI, agents, safety, evaluation, APIs, careers, and an extended study-architect showcase — with labs and game-style practice.
Open part 1
AI in Banking & Finance
Industry-depth track spanning fraud, credit, strategy, AML/KYC, service, collections, open banking, cyber, regtech, advice copilots, validation, vendors, and a board-ready operating-model capstone.
Open part 1
AI for Educators
Classroom-ready programme covering foundations, lesson design, feedback, policy, families, inclusion, STEM, language support, leadership dashboards, assessment design, PLCs, safeguarding, rollout, and a smart-school capstone.
Open part 1
AI in Industrial Operations
Heavy-industry track covering the AI tools in use today — CMMS-native predictive maintenance, computer vision inspection, SCADA/IIoT analytics, digital twins, process-plant assistants, quality vision, energy optimisation, OT cyber, and an integrated industrial AI roadmap.
Open part 1
AI for Developers
Technical pathway from mathematics for ML through classical algorithms, neural networks, PyTorch/TensorFlow, training and evaluation, transformers and LLM APIs, feature pipelines, computer vision and NLP code, deployment, responsible AI, and a production-style developer capstone.
Open part 1