Machine Learning for Executives Training Course

Participants can frame a business problem as a testable machine learning use case.

• Problem statements linking operational pain points to measurable outcomes
• Target variables defined from decisions, events and historical records
• Prediction horizons matched to intervention timing and decision cycles
• Unit of analysis selected for customers, transactions, assets or cases
• Baseline process performance established before model investment approval
• False-positive and false-negative costs quantified for decision thresholds

Participants can prioritise candidate use cases using a transparent portfolio method.

• Use-case canvases capturing users, decisions, data and expected benefits
• Value-feasibility-risk matrix for initial machine learning portfolio ranking
• Data availability assessed before estimating model development effort
• Decision frequency and scale used to estimate economic potential
• Dependency mapping across process owners, systems, suppliers and controls
• Portfolio sequencing between quick wins, foundations and strategic bets

Participants can assess whether available data is fit for a proposed machine learning application.

• Data lineage traced from source system through transformation and consumption
• Completeness, accuracy, timeliness and consistency assessed using data-quality dimensions
• Historical labels tested for reliability, delay and operational bias
• Missing-data patterns examined before selecting imputation or exclusion methods
• Training data representativeness checked against intended production populations
• Data access constraints identified across privacy, security and ownership

Participants can explain how supervised learning supports classification and regression decisions.

• Classification models predict categories such as default, fraud or churn
• Regression models estimate continuous values such as demand or cost
• Training, validation and test sets prevent misleading performance claims
• Overfitting identified when training results exceed unseen-data performance
• Feature variables selected from plausible predictive signals and constraints
• Model outputs translated into operational scores, rankings and thresholds

Participants can identify appropriate uses and limitations of unsupervised learning methods.

• Clustering groups customers, transactions or assets without predefined labels
• K-means assumptions and limitations for segmenting operational populations
• Dimensionality reduction summarises high-volume variables for visual inspection
• Anomaly detection identifies unusual patterns requiring human investigation
• Cluster profiles tested for stability, actionability and commercial meaning
• Unsupervised outputs treated as hypotheses rather than confirmed findings

Participants can challenge feature choices and identify common sources of data leakage.

• Feature engineering converts raw records into meaningful predictive variables
• Time windows prevent future information entering historical training records
• Data leakage detected through implausibly strong validation performance results
• Proxy variables reviewed for hidden sensitive attributes and unfair effects
• Labels aligned with the actual decision outcome and timing
• Feature availability confirmed at the moment operational decisions occur

Participants can interpret model performance reports and select business-appropriate thresholds.

• Confusion matrices show correct and incorrect classification outcomes
• Precision and recall matched to investigation capacity and risk appetite
• ROC-AUC measures ranking quality across alternative classification thresholds
• Calibration checks whether predicted probabilities match observed event rates
• Lift charts compare model targeting against random selection baselines
• Decision thresholds set using benefit, cost and operational capacity

Participants can assess explainability and fairness requirements for machine learning decisions.

• Global explainability identifies variables driving overall model predictions
• Local explanations support review of individual high-impact decisions
• SHAP values interpreted for feature contribution and explanation consistency
• Fairness metrics compared across protected and operationally relevant groups
• Disparate impact assessed before deploying customer-facing decision models
• Human review routes defined for contested, vulnerable or exceptional cases

Participants can specify core privacy, security and legal controls for machine learning initiatives.

• Purpose limitation links personal data use to documented business objectives
• Data minimisation restricts model inputs to necessary predictive information
• Privacy impact assessments identify risks before sensitive-data processing begins
• Access controls separate data preparation, model development and production operations
• Retention schedules address training datasets, features and prediction records
• Third-party contracts define data processing, ownership and audit rights

Participants can establish governance controls across the machine learning lifecycle.

• Model inventory records purpose, owner, users, data and risk tier
• Three-lines model assigns ownership, challenge and independent assurance responsibilities
• Approval gates require evidence before pilot, deployment and material changes
• Model documentation records assumptions, limitations, validation and intended use
• NIST AI RMF functions organise govern, map, measure and manage activities
• Escalation criteria trigger review after incidents, drift or control failures

Participants can oversee a staged machine learning delivery plan from discovery through production.

• CRISP-DM phases connect business understanding, data and modelling activities
• Discovery decisions define sponsors, users, constraints and success measures
• Proof-of-concept scope tests uncertain assumptions with limited operational exposure
• Pilot design measures model value against current process performance
• Production readiness covers integration, security, support and user training
• Exit criteria determine whether to scale, redesign or stop

Participants can assess how models are deployed, supported and embedded in business processes.

• Batch scoring, real-time APIs and embedded analytics deployment patterns
• MLOps pipelines manage versioning, testing, release and rollback controls
• Model registry records approved versions, owners and deployment status
• Workflow integration assigns decisions between model outputs and human judgement
• Service-level requirements cover latency, availability, recovery and support
• Change management prepares users for revised decisions and accountability

Participants can define monitoring measures and maintenance actions for deployed machine learning models.

• Data drift detects changes in incoming feature distributions over time
• Concept drift identifies changing relationships between inputs and outcomes
• Performance monitoring compares predictions with later realised outcomes
• Fairness monitoring tracks outcome differences after operational deployment
• Alert thresholds assign investigation responsibilities and response times
• Retraining decisions require validation, approval and controlled model release

Participants can evaluate machine learning vendors, platforms and internal delivery options.

• Build-buy-partner analysis compares capability, control, speed and cost
• Vendor due diligence examines training data, model evidence and security
• Evaluation criteria test integration, explainability, monitoring and portability
• Procurement statements specify acceptance tests, documentation and audit access
• Cloud platform choices consider data residency, cost and technical dependencies
• Contractual responsibilities distinguish supplier obligations from organisational accountability

Participants can construct a credible business case and benefits-tracking plan for machine learning.

• Benefit hypotheses link model outputs to measurable operational interventions
• Financial cases estimate revenue, loss avoidance, productivity and service effects
• Cost models include data engineering, licensing, integration and ongoing monitoring
• Sensitivity analysis tests assumptions about adoption, accuracy and intervention rates
• Benefits owners accept accountability for operational changes and outcomes
• Measurement plans separate model performance from realised business value

Participants can communicate machine learning decisions clearly to boards, executives and delivery teams.

• Executive briefings state decision required, value, risk and evidence
• Model cards summarise intended use, limitations, metrics and monitoring
• Decision-rights matrices clarify sponsor, owner, approver and challenge roles
• Risk appetite statements define unacceptable model outcomes and exposures
• Board reporting combines value measures, incidents, drift and remediation status
• Technical uncertainty communicated through scenarios, ranges and explicit assumptions

Participants can build, review and present a complete machine learning initiative pack for a selected business case.

• Use-case definition specifies decision, target, users and intervention pathway
• Data-readiness assessment identifies sources, labels, gaps and access constraints
• Model approach selects suitable methods, metrics and validation evidence
• Risk assessment documents fairness, privacy, security and operational controls
• Business case quantifies benefits, costs, dependencies and adoption measures
• Governance roadmap assigns approvals, monitoring, ownership and escalation actions