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Artificial Intelligence Fundamentals E-learning
Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behavior. The Artificial Intelligence Fundamentals certification tests a candidate’s knowledge and understanding of the terminology and general principles of AI.
The course covers benefits and challenges of ethical and sustainable robust Artificial Intelligence (AI); the basic process of Machine Learning (ML) – Building a Machine Learning (ML) Toolkit; the challenges and risks associated with an AI project, and the future of AI and Humans in work.
Upon completion of the self-study course, you will be optimally prepared for the official examination on Artificial Intelligence Fundamentals by Van Haren Publishing Group.
Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):
• Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI);
• Artificial Intelligence (AI) and Robotics;
• applying the benefits of AI projects – challenges and risks;
• Machine Learning (ML) Theory and Practice – Building a Machine Learning (ML) Toolbox;
• the Management, Roles and Responsibilities of Humans and Machines – The Future of AI.
Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):
• Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI);
• Artificial Intelligence (AI) and Robotics;
• applying the benefits of AI projects – challenges and risks;
• Machine Learning (ML) Theory and Practice – Building a Machine Learning (ML) Toolbox;
• the Management, Roles and Responsibilities of Humans and Machines – The Future of AI.
- Module 1: Introduction to AI Fundamentals
- Module 2: Ethical and Sustainable Human and Artificial Intelligence
- Module 3: Intelligent Agents & Robotics
- Module 4: AI Benefits, Challenges and Projects
- Module 5: Machine Learning (ML) Toolbox – Theory and Practice
- Module 6: Agile Working and The Future of Human and Machine collaboration
- Module 7: AI Fundamentals Wrap Up
- Module 8: Trial Exam
Exam Information
- 60 Multiple-choice questions
- 39 marks required to pass – 65%
- 60 minutes exam duration
- Closed book
There are no mandatory prerequisites.
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Overview
Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):
• Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI);
• Artificial Intelligence (AI) and Robotics;
• applying the benefits of AI projects – challenges and risks;
• Machine Learning (ML) Theory and Practice – Building a Machine Learning (ML) Toolbox;
• the Management, Roles and Responsibilities of Humans and Machines – The Future of AI. -
Learning outcomes
Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):
• Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI);
• Artificial Intelligence (AI) and Robotics;
• applying the benefits of AI projects – challenges and risks;
• Machine Learning (ML) Theory and Practice – Building a Machine Learning (ML) Toolbox;
• the Management, Roles and Responsibilities of Humans and Machines – The Future of AI. -
Course outlines
- Module 1: Introduction to AI Fundamentals
- Module 2: Ethical and Sustainable Human and Artificial Intelligence
- Module 3: Intelligent Agents & Robotics
- Module 4: AI Benefits, Challenges and Projects
- Module 5: Machine Learning (ML) Toolbox – Theory and Practice
- Module 6: Agile Working and The Future of Human and Machine collaboration
- Module 7: AI Fundamentals Wrap Up
- Module 8: Trial Exam
Exam Information
- 60 Multiple-choice questions
- 39 marks required to pass – 65%
- 60 minutes exam duration
- Closed book
-
Prequisites
There are no mandatory prerequisites.