AI Foundations: Concepts and Architecture
FoundationFour days that build a complete, structured picture of how modern AI works — from what a model is, to when a business genuinely needs to build one. No coding.
Program Overview
This is the depth course, for people who want to understand AI rather than operate one tool. Over four days you build a complete, structured picture. Day one is the landscape and how models actually work — types of AI, what a model is, machine learning versus data science, and the industries genuinely using each. Day two is the economics: tokens, cost, and then generative AI and the honest question of when a business needs Retrieval-Augmented Generation. Day three is agents and the full pipeline from data to model to deployment, with responsible AI worked as practice rather than recited. Day four is the decision most organisations get wrong — assistant or agent, existing model or custom build — and closes with a capstone where you map an AI opportunity in your own organisation end to end.
What You'll Learn
- Describe the AI landscape and the types of AI, and place any tool within it
- Explain what a model is, and how machine learning differs from data science
- Name the industries genuinely using each, and what for
- Explain tokens, and how usage and cost actually work
- Explain how generative AI works, and when a business truly needs RAG
- Distinguish an assistant from an agent, and know which a problem needs
- Describe the end-to-end pipeline from data to model to deployment
- Apply responsible AI as practice rather than recite it as principle
- Judge what can be achieved with existing models alone
- Decide when a business genuinely needs to build a custom model or agent
- Map an AI opportunity in your own organisation, end to end
Who Can Join
- •Students and career-switchers
- •Aspiring AI and machine-learning professionals
- •IT executives, analysts and product managers
- •Anyone who wants to understand AI, not just use a tool
PREREQUISITES
• None
• No coding required
• Curiosity and a willingness to be told what AI cannot do
Curriculum
→ What artificial intelligence is
→ The types of AI
→ The tool market
→ Placing a tool
→ Lab 0
Total: 27h 42m across 16 modules
Tools & Technologies
Projects Included
- ▹ Lab 0 — The AI landscape mapped, and every tool placed
- ▹ Lab M01 — Models and data explored across five industries
- ▹ Lab M02 — Token cost estimated for a real workload
- ▹ Lab M03 — Generative AI and RAG, tested against a real need
- ▹ Lab M04 — An assistant and an agent, built and compared
- ▹ Lab M05 — The end-to-end pipeline traced on a real system
- ▹ Lab M06 — Responsible AI applied to a live scenario
- ▹ Lab M07 — The current AI landscape assessed
- ▹ Lab M08 — Use cases judged: existing model, or build?
- ▹ Lab M09 — The build decision, made and defended
- ▹ Lab M10 — An AI opportunity mapped end to end
- ▹ Lab M11 — The capstone presented and challenged
- ▹ Lab M12 — Data quality and its consequences
- ▹ Lab M13 — Evaluation, honestly done
- ▹ Lab M14 — Deployment and what changes
- ▹ Lab M15 — Your ninety-day learning plan
Official Certifications
Assessment is continuous and practical: 16 Applied Learning Labs (60%) and an end-of-course practical assessment (40%). Passing mark 80%, plus completion of all 16 labs and full attendance. The CertTulen Course Completion Certificate is awarded on meeting that standard.
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