AI Basics (Artificial Intelligence) Self-Study Package

Algemene omschrijving

This e-learning contains the course materials.

The AI Basics (Artificial Intelligence) E-Learning offers a comprehensive introduction to AI for professionals in business and government. It covers the basics in AI for managers and decision makers in general.

Doelgroep

Everyone that wants or needs to have a basic understanding of data protection and European legal requirements as defined in the GDPR. The Essentials exam is exceptionally suitable for everyone that needs to make informed decisions regarding the privacy and the protection of their own data.

The AI Basics (Artificial Intelligence) E-Learning is particularly suitable for the following people:

• Business Leaders and Executives (CEOs, COOs, CTOs)
  • Understand AI’s role in strategy, digital transformation, and competitive advantage.
• Product and Innovation Managers
  • Learn how to integrate AI into products and drive innovation aligned with business goals.
• Operations and Supply Chain Managers
  • Focus on AI’s applications in process optimization, predictive analytics, and logistics.
• Finance and Risk Professionals
  • Understand AI for financial forecasting, fraud detection, and risk management.
• HR and People Managers
  • Use AI for recruitment, talent management, and employee engagement analytics.
• Entrepreneurs and Start-up Founders
  • Leverage AI for scaling and automating business processes cost-effectively.

Leerdoelen

By completing the AI Basics (Artificial Intelligence) E-Learning, you will gain the following knowledge and skills:

• Privacy and data protection fundamentals & regulation.
• Organizing data protection.
• Practice of data protection.

Voorkennis

You do not need any prior knowledge to access the AI Basics (Artificial Intelligence) E-Learning.

Onderwerpen

The AI Basics (Artificial Intelligence) E-Learning consists of these main parts:

1. Introduction to AI Basics.
2. Ethical and Sustainable Human and Artificial Intelligence.
3. AI Benefits, Challenges and Projects.
4. Machine Learning (ML) Toolbox – Theory and Practice.
5. Agile Working and The Future of Human and Machine collaboration.
6. AI Basics Wrap Up.
7. Trial Exam.