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Description

Machine Learning: Understanding the Methods, Assessing Models and Framing a Project

An accessible state of the art to judge whether a machine learning project is worth pursuing

  • 0.5 days — 3.5 h
  • In-person or virtual
  • Foundation
  • Up to 6 participants

Government entities regularly receive proposals for predictive tools without always having the reference points needed to assess them. Behind the term machine learning lie very different families of methods whose data requirements, transparency and error risks bear little comparison.

Two hours to set out an accessible state of the art: the main families of learning methods, what they require in terms of data, how model quality is measured and the questions to ask before launching a project. This foundation-level session requires no coding practice.

Learning objectives

  • Distinguish between supervised and unsupervised learning
  • Describe the data requirements of a learning project
  • Interpret the performance indicators of a model
  • Identify situations in which a model is not suitable
  • Formulate the questions to put to a vendor

What makes this programme different

A state of the art explained without mathematical formalism
Examples drawn from projects delivered in the public sector
A checklist of questions to ask before selecting a solution

Programme

1The Families of Methods

A map before the detail

  • Supervised and unsupervised learning
  • Regression, classification and clustering
  • Neural networks and deep learning
  • The place of large language models

2Data Before the Model

Without usable data, nothing holds

  • Building the dataset and assessing its quality
  • Representativeness and sampling bias
  • Separating training and evaluation data
  • Constraints relating to personal data

3Judging a Project

What to ask before committing

  • Measuring the performance of a model
  • Explainability and acceptability of decisions
  • Deployment and maintenance costs
  • Questions to put to the vendor

Who is it for

Public sector officers and project leads as well as data owners and decision-makers faced with proposals for predictive tools.

Prerequisites

No prerequisites

Dates & locations

36 scheduled dates between November 2026 and December 2027. Seats are confirmed in the order enquiries are received.

November 2026

December 2026

January 2027

February 2027

March 2027

April 2027

May 2027

June 2027

September 2027

October 2027

November 2027

December 2027

None of these dates suit you? We open additional sessions on request, and any programme can be run privately for your team.

Practical details

Before the programme
Online positioning questionnaire. Your development objectives are shared with the trainer, who tailors the practical case studies to your context.
Teaching methods
Theoretical input, workshops and practical case studies. Digital course materials and method sheets provided.
Assessment
Multiple-choice tests and role-play exercises. Assessment of learning at the start and end of the programme, with immediate and 60-day follow-up evaluations.
After the programme
One year of access to the e-learning platform. Self-assessment of the skills acquired and a 30-day follow-up session with your trainer.
How to register
Registration online or on the basis of a quotation.
Lead time
11 working days after confirmation of registration.
Accessibility
Accessible to people of determination. Contact our accessibility coordinator to design a suitable solution: contact@mpf-academy.ae
Start dates
Rolling intake: in addition to the scheduled sessions, this programme can start on request.