A structured path through artificial intelligence from the ground up
The program covers how AI systems are built, how they fail, and how to work with them productively. Five modules, real datasets, and instructors who work in the field.
see the masterclass schedule
What the program covers
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1
Foundations of AI Thinking
How machine learning models are structured, how data shapes decisions, and where AI systems succeed or fail. No assumed background.
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2
Supervised and Unsupervised Methods
Classification, regression, and clustering tasks using annotated real-world datasets. Each exercise includes a worked example before the hands-on portion.
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3
Neural Networks in Practice
Build and test small network architectures. The focus is on understanding how layers, weights, and activation functions interact — not on memorising formulas.
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4
Prompt Engineering and Language Models
How large language models process input and how prompt structure changes output quality. Includes comparison exercises across different model families.
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5
Evaluation and Responsible Use
Apply metrics to assess model performance and examine bias, fairness, and practical deployment constraints. Includes case study analysis from production systems.
Where participants typically start — and where they land
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Do I need a programming background to join?
No prior programming experience is required. The program starts from first principles and introduces technical concepts gradually. Familiarity with spreadsheets or basic data handling helps but is not a prerequisite.
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How long does the program take to complete?
Most participants complete the full program in 8 to 12 weeks working at roughly 4 to 6 hours per week. The material is self-paced, so you can move faster or slower depending on your schedule.
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Is the content updated as AI tools evolve?
Yes. Core modules are reviewed and updated on a rolling basis. When significant changes occur in the field — new model families, revised benchmarks, updated tooling — the relevant sections are revised and participants are notified.
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What does the certificate confirm?
The certificate confirms that you completed all modules and passed the practical assessments at the end of each unit. It does not claim professional accreditation but does reflect genuine engagement with the material.
Instructors
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Tobias Wrenfield
ML systems, modules 1–3
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Nadia Ostroff
LLMs and evaluation, modules 4–5
Format at a glance
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Self-paced video
Watch and rewatch each lesson on your schedule
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Practical exercises
Each module ends with a graded hands-on task
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Live Q&A sessions
Bi-weekly open calls with instructors, recorded for replay
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Certificate on completion
Issued after all assessments are submitted and reviewed