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AI Foundations for Non-Technical Professionals

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.

5.922€*
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Level:
advanced
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Duration:
21
Hours (days:
3
)
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Training customized to your needs
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Immersive hands-on experience in a dedicated setting
*Price can range depending on number of participants, change of outline, location etc.
Get Quote

Master new skills guided by experienced instructors from anywhere.

4.587€*
Graph Icon - Education X Webflow Template
Level:
advanced
Clock Icon - Education X Webflow Template
Duration:
21
Hours (days:
3
)
Camera Icon - Education X Webflow Template
Training customized to your needs
Star Icon - Education X Webflow Template
Reduced training costs
*Price can range depending on number of participants, change of outline, location etc.
Get Quote