How AI Algorithms Work

  • How AI Algorithms Work
  • Online

How AI Algorithms Work

Course Category : Information Technology

A High-Level Professional Training Program Focused on Strategic and Conceptual Mastery, that demystifies AI algorithms for non-technical professionals, enabling an informed understanding, critical thinking, and responsible use in organizational decision-making.
Duration: 5 Days | Level: Introductory – Intermediate.

Starts On

1 - March - 2026

Ends On

5 - March - 2026

Location

Online

Language

Arabic

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View Course Introductory PDF

Targeted Audience

  • Executives and Decision-Makers
  • Middle Management Leaders
  • Digital Transformation and Innovation Managers
  • Human Resources Managers
  • Marketing and Sales Managers
  • Finance and Risk Managers
  • Non-Technical Consultants
  • Professionals seeking AI understanding without a technical background

Targeted Skills

  • Understanding How AI Algorithms Function
  • Distinguishing Major Algorithm Types
  • Interpreting AI Outputs and Recommendations
  • Recognising Algorithm Limitations and Risks
  • Asking the Right Questions to Technical Teams
  • Supporting Decisions with Realistic AI Insight

Expected Outcomes

  • Gain a clear, non-technical understanding of AI algorithms.
  • Distinguish between machine learning, deep learning, and rule-based systems.
  • Interpret AI outputs without technical expertise.
  • Recognise AI limitations, bias, and risks.
  • Improve communication with technical teams.
  • Make informed decisions about AI adoption.

Training Topics Index

  • Algorithm concept explained simply
  • Traditional programming vs AI
  • Why data matters
  • AI as a decision support tool
  • The “black box” idea

  • Data as AI fuel
  • Learning from examples
  • Training vs deployment
  • Predictions and probabilities
  • Common learning errors

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Simplified deep learning
  • Use-case suitability

  • From data to decision
  • Recommendations and predictions
  • Business-level interpretation
  • Accuracy vs confidence
  • Explainability limits

  • Algorithmic bias
  • Over-reliance on AI
  • Errors and misuse
  • When AI is not suitable
  • Human oversight