Machine Learning & Predictive Analytics for Decision-Makers
The expensive machine learning failures are rarely technical — they are commissioning failures: the wrong problem framed, the wrong claim believed, the wrong metric celebrated. The decision-maker does not need to build models; the decision-maker needs to interrogate them — and that is a two-day skill.
Format
Classroom · Virtual
Upcoming sessions
Pick a session to applyADMISSIONS OPENThe programme
This two-day machine learning course for executives and managers contains no code and no mathematics beyond percentages, yet is rigorous where decision-makers need rigour. It builds an honest picture of what machine learning and predictive analytics can and cannot do: pattern-finding, not understanding; prediction from history, biases included. Participants learn to frame business problems as prediction problems — what is predicted, on what data, at what cost when wrong; to read model outputs like an informed buyer — accuracy and its traps, false positives versus negatives, confidence and drift; and to interrogate vendor and internal AI claims — questions that expose the overfitted demo, unrepresentative pilot or flattering metric. It closes with governance essentials — accountability, bias, explainability, monitoring — sized for a leadership team, not a compliance department. Case-led, it runs in open enrolment and as a private in-house briefing for leadership teams evaluating live proposals. Delivered in English and French across the Middle East, Africa and Asia — Dubai, Doha, Nairobi, Johannesburg, Singapore — and live online.
What you will do
Who attends
Executives, heads of function and senior managers who sponsor, fund or approve analytics and AI initiatives; product, operations, risk, marketing and finance leaders receiving vendor proposals; board members and directors who must challenge AI strategy credibly; and programme leaders who sit between the data science team and the business. No technical background is required or expected. Leadership teams frequently take the programme together in-house, applied directly to the proposals on their own table.
Programme agenda
Built for the decisions no textbook prepares you for
I.What machine learning actually is
- Learning from history: pattern-finding, prediction and the absence of understanding
- The main families in plain language: classification, regression, clustering, generative models
- Honest boundaries: the problems machine learning is structurally bad at
II.Framing the prediction problem
- From business pain to prediction target: what exactly are we forecasting?
- Data reality: what history you actually have, and the biases baked into it
- The decision and the cost of error: where the model’s output actually lands
III.Reading model results
- Why 95% accuracy can be worthless: base rates and the accuracy trap
- False positives against false negatives: choosing the error you can live with
- Overfitting, drift and why the pilot rarely predicts production
IV.Interrogating AI claims
- The vendor questions: training data, test conditions, metric selection and maintenance
- Reading a case study the way a diligence team would
- Casework: real proposals dissected — which claims survive scrutiny
V.Value & the use-case portfolio
- Where predictive analytics reliably pays: demand, risk, maintenance, churn, pricing
- Sequencing the portfolio: quick proof, durable value, honest kill criteria
- Build, buy or wait: the sponsor’s decision framework
VI.Governance for the leadership team
- Accountability: who answers for a model’s decisions, in writing
- Bias, fairness and explainability at the level leaders must own them
- Monitoring deployed models: the reporting a sponsor should demand
Frequently asked
Do I need any mathematics or coding for this course?
No. The programme contains no code and no mathematics beyond percentages, yet stays rigorous where decision-makers need rigour. Concepts such as classification, regression, overfitting and drift are taught in plain language through cases, so participants can explain them to a board and challenge them in a proposal without any technical background.
Will I learn to build machine learning models?
Deliberately not. The course makes you a competent buyer, sponsor and challenger of machine learning rather than a builder: framing business problems as prediction problems, reading model outputs like an informed buyer, interrogating vendor and internal claims, and setting the governance essentials a leadership team must own. The expensive failures it prevents are commissioning failures, not coding ones.
Can our leadership team take the programme together?
Yes — leadership teams frequently take it as a private in-house briefing, applied directly to the analytics and AI proposals on their own table. The case material is tailored to the institution and its live decisions, and delivery is available in English or French, on site or live online.
When does the course run, and what does it cost?
Open-enrolment sessions run on a rolling calendar across the Middle East, Africa and Asia — including Dubai, Doha, Nairobi, Johannesburg and Singapore — and live online; dates are confirmed on request. Fees for open seats and quotations for private leadership briefings are provided on enquiry. The course is delivered in English and French.
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One request — the full agenda, the faculty and the next cohort dates, sent personally by the admissions team.