AI and Machine Learning Foundations Course

Name: AI and Machine Learning Foundations for Professionals Course
Availability: InStock

Frame workplace use cases, assess data and model evidence, and define responsible adoption decisions without requiring programming.

AI and Machine Learning Foundations Course

At a glance

Duration: 5 days
Format: Classroom
Cities: London, Abu Dhabi, Langkawi, Dubai, Cairo, San Diego and more
Next session: 12 – 16 October 2026, London
Average fee: 5,800 €

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AI and Machine Learning Foundations for Professionals Course

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Overview

AI and Machine Learning Foundations for Professionals Course is a five-day foundation course for professionals responsible for business improvement, data decisions, technology adoption, operations, risk, or project delivery, who leave with an AI and Machine Learning Adoption Roadmap. Participants distinguish AI methods, frame use cases, assess data readiness, interpret model evaluation, map lifecycle decisions, define human oversight, and plan responsible adoption. Agile Leaders Training Center provides training in AI and machine learning foundations.

Who Should Attend

Business teams responsible for identifying improvement opportunities and outcomes
Data teams responsible for information quality and analytical decisions
Technology teams responsible for solution assessment and integration
Operations teams responsible for process performance and controls
Risk teams responsible for oversight, impacts, and accountability
Project teams responsible for coordinating adoption and stakeholder decisions

The course assumes participants contribute to business, data, technology, operations, risk, or project decisions and leaves out programming, mathematical derivations, specialist model engineering, data-science certification, and product configuration.

Departments and Industries

The course supports responsible AI and machine learning adoption across functions and sectors.

Strategy, transformation, and innovation functions
Data, analytics, and information-management teams
Technology, digital-service, and operations functions
Financial, healthcare, and professional-service organizations
Manufacturing, energy, and logistics organizations
Government, education, and nonprofit organizations

Learning Objectives

By the end of this course, participants will be able to:

Compare AI, machine learning, and deep learning methods
Apply a use-case framing method to business needs
Analyze data readiness, features, labels, and limitations
Evaluate model results with task-appropriate metrics
Use lifecycle, oversight, and risk-control tools
Build an AI and Machine Learning Adoption Roadmap

Course Agenda

Day 1: AI and Machine Learning Context

AI, Machine Learning, and Deep Learning Comparison Map
Supervised, Unsupervised, and Reinforcement Learning Decision Tree
Prediction, Classification, Clustering, and Recommendation Use-Case Cards
Human Task and Machine Task Boundary Canvas
AI Value, Feasibility, and Risk Screening Matrix

Day 2: Use Cases and Data Readiness

Business Problem and Outcome Framing Template
Stakeholder, User, and Decision Journey Map
Data Source, Ownership, and Provenance Inventory
Feature, Label, and Target Definition Sheet
Data Quality, Bias, and Representativeness Checklist

Day 3: Model Lifecycle and Evaluation

Training, Validation, and Test Data Partition Diagram
Baseline and Candidate Model Comparison Table
Accuracy, Precision, Recall, and Error-Cost Scorecard
Overfitting, Generalization, and Drift Diagnostic Guide
Model Deployment and Monitoring Lifecycle Map

Day 4: Responsible Adoption and Oversight

NIST AI RMF Govern, Map, Measure, and Manage Worksheet
Human Oversight and Escalation Decision Matrix
AI Impact, Risk, and Control Register
Model Documentation and Evidence Checklist
Adoption Readiness, Ownership, and Dependency Dashboard

Day 5: AI Adoption Planning Practice

Suggested Exercise: Compare AI and Machine Learning Approaches
Suggested Exercise: Frame and Screen a Workplace Use Case
Suggested Exercise: Review Data and Model Evaluation Evidence
Suggested Exercise: Define Oversight, Controls, and Monitoring
Capstone Exercise: AI and Machine Learning Adoption Roadmap

Practical Exercises

The course uses suggested activities that turn foundation concepts into traceable adoption decisions.

Suggested activity: classify workplace problems by AI method, decision type, expected outcome, feasibility, and risk
Suggested activity: frame a use case and inspect sources, ownership, features, labels, quality, bias, and representativeness
Suggested activity: compare validation evidence, task metrics, error costs, generalization concerns, deployment, and monitoring needs
Suggested activity: assign human oversight, risks, controls, evidence, ownership, dependencies, and adoption actions

FAQs

Who suits AI and machine learning foundations training?

AI and machine learning foundations training suits professionals who shape business, data, technology, operations, risk, or project decisions. It assumes workplace decision experience and requires no programming or model-building background.

How do AI and machine learning foundations differ from technical data-science training?

AI and machine learning foundations focus on concepts, use cases, data readiness, evaluation evidence, oversight, and adoption decisions. Technical data-science training focuses on mathematics, coding, algorithms, experimentation, and model implementation.

How should professionals select an AI or machine learning use case?

Professionals should connect a defined decision or process problem to an appropriate method, available data, measurable outcome, affected stakeholders, feasibility constraints, risks, and human accountability.

What model evaluation evidence should non-specialists review?

Non-specialists should review data partitions, baselines, task-appropriate metrics, error costs, performance across relevant groups, generalization limits, monitoring thresholds, and documented assumptions.

What belongs in an AI and Machine Learning Adoption Roadmap?

The roadmap includes prioritized use cases, outcomes, data needs, lifecycle stages, evaluation criteria, human oversight, risks, controls, owners, dependencies, monitoring, and sequenced adoption actions.

Conclusion

Participants take back an AI and Machine Learning Adoption Roadmap connecting use cases, data, models, evaluation, oversight, and controls. The roadmap makes assumptions, evidence, ownership, dependencies, and decisions visible across participating functions. It supports staged adoption based on business value, feasibility, risk, and monitoring needs.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 21-40 of 76 events

Location	Dates	Duration	Mode	Price
Trabzon	Week 01, 2027 10 – 14 January 2027	5 Days	Onsite	€6,800
Amsterdam	Week 02, 2027 11 – 15 January 2027	5 Days	Onsite	€5,700
Kuala Lumpur	Week 04, 2027 25 – 29 January 2027	5 Days	Onsite	€5,200
Prague	Week 04, 2027 25 – 29 January 2027	5 Days	Onsite	€6,000
Istanbul	Week 05, 2027 1 – 5 February 2027	5 Days	Onsite	€4,500
Lisbon	Week 05, 2027 1 – 5 February 2027	5 Days	Onsite	€5,700
Zanzibar	Week 05, 2027 7 – 11 February 2027	5 Days	Onsite	€5,500
Tbilisi	Week 07, 2027 15 – 19 February 2027	5 Days	Onsite	€5,000
Jakarta	Week 07, 2027 15 – 19 February 2027	5 Days	Onsite	€5,700
Vienna	Week 08, 2027 22 – 26 February 2027	5 Days	Onsite	€5,700
Munich	Week 08, 2027 22 – 26 February 2027	5 Days	Onsite	€5,700
Dubai	Week 10, 2027 8 – 12 March 2027	5 Days	Onsite	€4,500
Abu Dhabi	Week 10, 2027 8 – 12 March 2027	5 Days	Onsite	€4,700
Paris	Week 11, 2027 15 – 19 March 2027	5 Days	Onsite	€5,700
Montreux	Week 11, 2027 15 – 19 March 2027	5 Days	Onsite	€7,500
Rome	Week 12, 2027 22 – 26 March 2027	5 Days	Onsite	€5,700
Bangkok	Week 12, 2027 28 March – 1 April 2027	5 Days	Onsite	€6,000
London	Week 14, 2027 5 – 9 April 2027	5 Days	Onsite	€5,700
Berlin	Week 14, 2027 5 – 9 April 2027	5 Days	Onsite	€5,700
Seoul	Week 15, 2027 12 – 16 April 2027	5 Days	Onsite	€10,000

Frequently asked questions

What does this course cover?

OverviewAI and Machine Learning Foundations for Professionals Course is a five-day foundation course for professionals responsible for business improvement, data decisions, technology adoption, operations, risk, or project delivery, who leave with an AI and Machine Learning Adoption Roadmap. Participants distinguish AI methods, frame use cases, assess dat…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

AI and Machine Learning Foundations Course