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.

This 1-week course has 77 dates from November 2026 to November 2027 in 54 cities and live online. The next date runs 16 – 20 November 2026 in Amsterdam. Fees per participant run from €4,100 (Casablanca) to €14,000 (San Diego) in a classroom, and from €1,500 live online.

AI and Machine Learning Foundations Course

Duration: 1 Week5 days
Next start: 16 November 2026Amsterdam
Scheduled dates: 77
Classroom cities: 54Amsterdam, Chicago, Casablanca, Lagos and more
Format: Classroom and live online
Fee per participant: €1,500–€14,000Average €5,750

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AI and Machine Learning Foundations for Professionals Course

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What does this course cover?

Overview

AI and Machine Learning Foundations for Professionals Course is a one-week 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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When and where does this course run?

Showing 1-20 of 77 events

Location	Dates	Duration	Mode	Price
Amsterdam	Week 47, 2026 16 – 20 November 2026	1 Week	Onsite	€5,700
Chicago	Week 47, 2026 22 – 26 November 2026	1 Week	Onsite	€12,000
Casablanca	Week 48, 2026 23 – 27 November 2026	1 Week	Onsite	€4,100
Lagos	Week 48, 2026 29 November – 3 December 2026	1 Week	Onsite	€4,100
Amman	Week 49, 2026 6 – 10 December 2026	1 Week	Onsite	€4,100
Vienna	Week 50, 2026 7 – 11 December 2026	1 Week	Onsite	€5,700
Tashkent	Week 50, 2026 13 – 17 December 2026	1 Week	Onsite	€4,500
Dubai	Week 52, 2026 21 – 25 December 2026	1 Week	Onsite	€4,500
Abu Dhabi	Week 52, 2026 21 – 25 December 2026	1 Week	Onsite	€4,700
Johannesburg	Week 52, 2026 27 – 31 December 2026	1 Week	Onsite	€4,500
London	Week 53, 2026 28 December 2026 – 1 January 2027	1 Week	Onsite	€5,700
Dubai	Week 01, 2027 4 – 8 January 2027	1 Week	Onsite	€4,500
Trabzon	Week 01, 2027 10 – 14 January 2027	1 Week	Onsite	€6,800
Amsterdam	Week 02, 2027 11 – 15 January 2027	1 Week	Onsite	€5,700
Kuala Lumpur	Week 04, 2027 25 – 29 January 2027	1 Week	Onsite	€5,200
Prague	Week 04, 2027 25 – 29 January 2027	1 Week	Onsite	€6,000
Istanbul	Week 05, 2027 1 – 5 February 2027	1 Week	Onsite	€4,500
Lisbon	Week 05, 2027 1 – 5 February 2027	1 Week	Onsite	€5,700
Zanzibar	Week 05, 2027 7 – 11 February 2027	1 Week	Onsite	€5,500
Tbilisi	Week 07, 2027 15 – 19 February 2027	1 Week	Onsite	€5,000

Frequently asked questions

When does this course next run?

The next date runs 16 – 20 November 2026 in Amsterdam. It is one of 77 dates scheduled up to November 2027.

Where is this course held?

In a classroom in 54 cities: Amsterdam, Chicago, Casablanca, Lagos, Amman, Vienna, Tashkent, Dubai, and more. It is also scheduled as live online sessions.

Can I attend this course online?

Yes. One date is scheduled as a live online session: 4 – 8 October 2027, from €1,500 per participant.

How long is the course?

1 Week. Each date shows its start and end dates.

How much does the course cost?

Fees per participant run from €4,100 (Casablanca) to €14,000 (San Diego) in a classroom, and from €1,500 live online. Each date shows its own fee.

What does this course cover?

OverviewAI and Machine Learning Foundations for Professionals Course is a one-week 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.

Can this course be run for our team?

Yes. Tell us the number of participants and your preferred dates and city through our training request form.

AI and Machine Learning Foundations Course