Artificial Intelligence and Machine Learning

  • Artificial Intelligence and Machine Learning
  • Egypt - Cairo

Artificial Intelligence and Machine Learning

Course Category : Information Technology

A professional, practice-oriented programme designed to equip participants with a solid understanding of Artificial Intelligence and Machine Learning as strategic tools for decision-making, performance optimisation, and the development of intelligent, real-world business solutions.
Duration: 5 Days
Level: Intermediate to Advanced.

Starts On

1 - November - 2026

Ends On

5 - November - 2026

Location

Egypt - Cairo

Language

English

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View Course Introductory PDF

Targeted Audience

  • Middle management professionals seeking to understand AI-driven business applications
  • Digital transformation and innovation managers
  • Data analysts and information systems professionals
  • Operations, quality, and performance improvement managers
  • Executives aiming to enhance data-driven decision-making

Targeted Skills

  • Understanding core AI and Machine Learning concepts
  • Data analysis and predictive modelling
  • Selecting and evaluating Machine Learning algorithms
  • Interpreting model outputs for decision-making
  • Managing AI risks and ethical considerations

Expected Outcomes

  • Distinguish between Artificial Intelligence, Machine Learning, and Deep Learning.
  • Prepare and analyse data for Machine Learning applications.
  • Select appropriate models based on problem type and data structure.
  • Evaluate model performance using standard metrics.
  • Apply practical AI use cases in business environments.
  • Integrate AI solutions into organisational strategies

Training Topics Index

  • Evolution and strategic importance of AI
  • Types and maturity levels of AI
  • AI vs ML vs Deep Learning
  • Industry-wide applications

  • Data types and sources
  • Data cleaning and preparation
  • Exploratory data analysis
  • Data quality and model impact

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Practical use cases

  • Performance evaluation metrics
  • Overfitting and underfitting
  • Model interpretation
  • Deploying practical solutions

  • AI ethics
  • Bias and privacy risks
  • AI governance frameworks
  • Future of AI and sustainability