Learning Tracks

Role-based tracks with real-world labs, decks, and interactive cards. Pick your path.

Design, build, evaluate, and operate production AI systems

•No prior machine-learning study is required; complete AI Engineering Foundations before Classical Machine Learning
•Working Python knowledge: functions, classes, packages, virtual environments, testing, and debugging
•Command-line and Git proficiency, plus working knowledge of HTTP and JSON
•Working familiarity with pandas or another tabular-data stack, practical SQL, web APIs, and asynchronous Python; complete the course-provided bridge before the second lab for any missing area
•Foundational algebra, probability, statistics, and linear algebra; the track applies these foundations rather than replacing a mathematics course

Build safe internal tools with AI assistance

•No prior coding or Python experience is required when using the provided approved starter, managed workspace, and facilitator support
•Basic computer skills: managing files, using browser tabs, copying and pasting, and signing in to an approved workspace
•Ability to describe an everyday work task step by step in Arabic or English
•Agreement to use only course-provided synthetic data and approved sandbox services; no operational, personal, confidential, or secret data

Shape responsible AI portfolios, pilots, and adoption

•No coding or data-science background is required
•Familiarity with a service, operational process, or policy area that could be improved
•Spreadsheet numeracy: percentages, rates, baselines, simple cost-benefit calculations, and scenario comparisons
•Authorized access to a course case or equivalent evidence, including process measures, cost assumptions, stakeholders, and known constraints
•Ability to work with stakeholders, incomplete evidence, assumptions, and trade-offs

Turn reliable data into measurable AI and analytical decisions

•Working Python 3.12+ knowledge, including pandas, NumPy, scikit-learn, plotting, functions, environments, and debugging
•Practical SQL for filtering, joins, grouping, aggregation, and checking tabular data
•Foundational descriptive and inferential statistics, probability, sampling, uncertainty, and experiment concepts
•Ability to work in notebooks and the command line, manage environments, and use Git for versioned analytical work
•Ability to explain analytical findings, uncertainty, and limitations to a non-technical stakeholder

Shape trustworthy AI products around real user outcomes

•Experience defining products, services, or operational improvements
•Familiarity with user research, prioritization, and measurable success criteria
•Working literacy in AI-system boundaries, data dependence, probabilistic behavior, human oversight, and failure modes; complete the mandatory prework if these are new
•Quantitative evaluation literacy covering baselines, rates, thresholds, uncertainty, experiment comparisons, and guardrail metrics; complete the mandatory prework if needed
•Ability to collaborate with design, data, engineering, legal, and operations stakeholders

Design clear, inclusive, and trustworthy AI experiences

•Basic UX/UI knowledge, including task flows, wireframes, interaction states, and usability principles
•Working proficiency with a prototyping tool and interactive components, or completion of the mandatory prototyping prework
•Access to Arabic and English content review and accessibility review support; course-provided reviewers may fulfill this requirement
•Willingness to test designs with diverse users and revise assumptions from evidence