AI Foundations for Non-Technical Professionals Training

AI Foundations for Non-Technical Professionals

Build a practical, beginner-friendly understanding of modern AI. Across three days of short explanations, demos, and guided practice, participants explore deep neural networks, generative AI, NLP, computer vision, multimodal systems, smart agents, and explainable AI. The course culminates in a personal AI project that applies responsible AI principles to a real use case.

What will you learn?

You will understand the major AI model families, how they work at a conceptual level, and how to apply them responsibly to real problems. You will scope and build a small end-to-end AI solution using accessible tools, then present a project plan and prototype.

After this training you will be confident in:

Explaining the strengths and limits of deep neural networks, generative models, NLP, computer vision, and multimodal systems
Identifying viable use cases, mapping data needs, and choosing build vs buy approaches
Applying prompt design, retrieval augmented generation concepts, and basic agent workflows
Reading and explaining model behavior using plain language explainability and simple evaluation
Recognizing AI downsides including bias, privacy risk, security threats, and misuse
Implementing responsible AI strategies that consider ethics, law, governance, and organizational impact
Designing and presenting an end-to-end AI solution aligned to business or social outcomes

Requirements:

No coding required
Comfortable using web apps and spreadsheets
Bring a simple use case idea and any non-sensitive sample data you can share

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

Module 1: Orientation and the AI landscape

What AI can and cannot do for non-technical teams
Model families at a glance: classical ML, deep learning, transformers, diffusion
Where generative, multimodal, and agentic systems fit

Module 2: Data basics for AI literacy

Datasets, quality, labeling, and data minimization
Sensitive data, privacy, and consent in plain language
Intro to evaluation concepts: accuracy, precision and recall, false positives vs false negatives

Module 3: Deep neural networks explained

Layers, parameters, and the training loop in conceptual terms
CNNs, RNNs, and transformers at a high level
Embeddings and why they power search, clustering, and retrieval

Module 4: Generative AI fundamentals

Language models for drafting, summarizing, and Q&A
Image and audio generation in plain language, including diffusion basics
Prompt patterns, content controls, and starting a personal project scope

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Module 5: NLP in practice

Classification, extraction, and summarization use cases
Retrieval augmented generation to ground outputs in your data
Multilingual and domain adaptation considerations

Module 6: Computer vision in practice

Image classification, object detection, OCR, and quality limits
Data needs for images and how to avoid shortcut learning
Realistic success criteria for vision pilots

Module 7: Smart agents and workflow automation

When to use agents to orchestrate tools and data
Guardrails, constraints, and handoffs to humans
Measuring reliability and defining safe failure modes

Module 8: Explainability, ethics, and law

Plain language explainability and simple inspection techniques
Bias, fairness, and representativeness of data
Legal and policy awareness: privacy, IP, disclosure, and governance checklists

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Module 9: Designing an AI solution

Problem framing, target outcomes, and stakeholder mapping
Data sourcing options, integration points, and cost considerations
Build vs buy choices and vendor evaluation criteria

Module 10: Personal project build and review

Translate your use case into a simple workflow and evaluation plan
Prepare or select non-sensitive data, prompts, and decision rules
Iterate on a prototype and document assumptions and risks

Module 11: Presentations, impact, and next steps

Share project outcomes and receive structured feedback
Operationalizing responsible AI: policies, roles, and monitoring basics
Roadmap for improvement, ROI measurement, and change management

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Hands-on learning with expert instructors at your location for organizations.

Level:

Advanced

Duration:

Hours (days:

)

Training customized to your needs

Immersive hands-on experience in a dedicated setting

*Price can range depending on number of participants, change of outline, location etc.

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Master new skills guided by experienced instructors from anywhere.

Level:

Advanced

Duration:

Hours (days:

)

Training customized to your needs

Reduced training costs

*Price can range depending on number of participants, change of outline, location etc.

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You can participate in a Public Course with people from other organisations.

/per trainee

Thanks for the numbers, they could be going to your emails. But they're going to mine... Thanks ;D

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Level:

Advanced

Duration:

Hours (days:

)

Fits ideally for individuals and small groups

Networking opportunities with fellow participants.

*Price can range depending on number of participants, change of outline, location etc.

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AI Foundations for Non-Technical Professionals

What will you learn?

Requirements:

Course Outline*:

Module 1: Orientation and the AI landscape

Module 2: Data basics for AI literacy

Module 3: Deep neural networks explained

Module 4: Generative AI fundamentals

Module 5: NLP in practice

Module 6: Computer vision in practice

Module 7: Smart agents and workflow automation

Module 8: Explainability, ethics, and law

Module 9: Designing an AI solution

Module 10: Personal project build and review

Module 11: Presentations, impact, and next steps

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AI Foundations for Non-Technical Professionals

Start your training experience here!