BIZNIQUE / AI & ML LEARNING
Practical learning for people who lead, apply or build AI and machine learning. Start at the right level and connect the concepts to meaningful work.
CHOOSE YOUR STARTING POINT
The programme should match the decisions people make and the experience they bring.
01 / LEADERS
Understand what AI can and cannot do, identify worthwhile use cases and ask better questions about evidence, ownership and risk.
Leave with: an opportunity brief and a practical evaluation checklist.
02 / PRACTITIONERS
Work through data preparation, analytical thinking and useful AI workflows. Learn how to inspect an answer, compare alternatives and recognise when review is needed.
Leave with: a worked example, an evaluation rubric and a workflow to practise.
03 / BUILDERS
Explore model workflows, information retrieval, evaluation and integration. Examine failure cases, access boundaries and the steps required to operate a useful system.
Leave with: a scoped lab, a test set and a plan for the next iteration.
THE LEARNING PATH
Choose the depth and sequence around your team. Topics can be taught as focused workshops or combined into a programme.
FOUNDATIONS
Statistics and uncertainty; vectors and matrices; data quality and exploratory analysis. Connect the ideas to the questions a model can answer.
MACHINE LEARNING
Features, supervised and unsupervised learning, model selection and suitable metrics. Practise separating training from evaluation and spotting information leakage.
APPLIED GENERATIVE AI
Task framing, grounding, retrieval and structured outputs. Compare representative examples, document limitations and place human review where it matters.
RELATED PRACTICE
Turn assumptions into scenarios, distinguish measures from summaries and use visual frameworks to structure a decision. Build habits that transfer across tools.
LEARNING BY DOING
A leader’s exercise: take one proposed AI use case and define the user, the failure cost and the evidence needed before a pilot.
An analyst’s exercise: compare an average of percentages with a ratio of totals, then explain which question each calculation answers.
A builder’s exercise: create a small evaluation set with ambiguous, missing and conflicting information; decide when the workflow should ask for review.
A FORMAT THAT FITS THE TEAM
Begin with the audience, starting level and the capability people should demonstrate. We can shape instructor-led sessions, hands-on exercises and follow-up practice around those goals.
Scope, delivery arrangements and prerequisites are agreed together. Exercises use suitable public or fictional material unless other access has been explicitly agreed.
EXPLORE THE MATHEMATICS
Explore 58 short lessons in linear algebra, calculus and optimisation. Each combines a small interactive example with a clear explanation, a practical limitation and a question you can check for yourself.
Move a value to see the calculation change, or use Animate to follow the transition. Work through a branch in order or choose the idea you need.
41 / LINEAR ALGEBRA
Vectors and matrices, scaling, projections, features, distances and factorisation.
15 / CALCULUS
Derivatives, the chain rule, optimisation steps, gradient flow and regularisation.
02 / OPTIMISATION
Feasible solutions, constrained objectives and Lagrange multipliers.
