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Welcome to Pink Elephant EMEA! We offer a comprehensive range of ITSM and ESM services designed to support your organisation's digital transformation.
Whether you're looking for expert ITSM consulting, cutting-edge technology solutions, award-winning training courses, or hands-on IT support, explore our website and use the search box to easily find the resources, insights, and services you need.
Whether you're looking for expert ITSM consulting, cutting-edge technology solutions, award-winning training courses, or hands-on IT support, explore our website and use the search box to easily find the resources, insights, and services you need.
Artificial Intelligence Basics E-learning
The AI Basics Certification is designed as a key credential for professionals who need to grasp the essentials of Artificial Intelligence (AI) and wish to apply it effectively in their business settings. This certification provides a good base into AI best practices, helping establish a shared understanding that can lead to substantial benefits.
This certification focuses on giving learners an overview of what AI is, how it functions, and its practical applications—along with the benefits and potential risks.
Upon completion of the self-study course, you will be optimally prepared for the official examination on Artificial Intelligence Basics by Van Haren Publishing Group.
Candidates should be able to demonstrate a knowledge and understanding in the following topics:
- What is AI? Explains the differences between human and artificial intelligence, examines Robert Dilt’s logical levels, and connects these concepts to various types of AI. It also discusses the impact of the 4th industrial revolution.
- Managing Data for AI: Introduces basic data literacy, explains different data types, and covers the importance of data semantics and syntax. It also addresses privacy concerns and the potential insights that can be derived from data.
- Ethics, Risks, and Trustworthiness: Explores the ethical challenges and risks associated with AI, including the importance of explainable AI. It introduces EU ethical guidelines and legislation, emphasizing the need to maintain public trust in AI technologies.
- Predictions, Algorithms, Machine, and Deep Learning: Provides an overview of various levels of prediction, key algorithms, common problem types, and generative AI models. It highlights which algorithms are best suited for specific types of problems
- Building and Assessing an AI Application: Outlines a basic approach to developing a simple AI application using the CRISP-DM methodology. It emphasizes the importance of understanding the business context and assessing reliability at each step of the process.
Candidates should be able to demonstrate a knowledge and understanding in the following topics:
- What is AI? Explains the differences between human and artificial intelligence, examines Robert Dilt’s logical levels, and connects these concepts to various types of AI. It also discusses the impact of the 4th industrial revolution.
- Managing Data for AI: Introduces basic data literacy, explains different data types, and covers the importance of data semantics and syntax. It also addresses privacy concerns and the potential insights that can be derived from data.
- Ethics, Risks, and Trustworthiness: Explores the ethical challenges and risks associated with AI, including the importance of explainable AI. It introduces EU ethical guidelines and legislation, emphasizing the need to maintain public trust in AI technologies.
- Predictions, Algorithms, Machine, and Deep Learning: Provides an overview of various levels of prediction, key algorithms, common problem types, and generative AI models. It highlights which algorithms are best suited for specific types of problems
- Building and Assessing an AI Application: Outlines a basic approach to developing a simple AI application using the CRISP-DM methodology. It emphasizes the importance of understanding the business context and assessing reliability at each step of the process.
- Module 1: Introduction to AI Fundamentals
- Module 2: Ethical and Sustainable Human and Artificial Intelligence
- Module 3: AI Benefits, Challenges and Projects
- Module 4: Machine Learning (ML) Toolbox – Theory and Practice
- Module 5: Agile Working and The Future of Human and Machine collaboration
- Module 6: AI Fundamentals Wrap Up
- Module 7: Trial Exam
Exam Information
- 20 Multiple-choice questions
- 13 marks required to pass – 65%
- 30 minutes exam duration
- Closed book
There are no mandatory prerequisites.
-
Overview
Candidates should be able to demonstrate a knowledge and understanding in the following topics:
- What is AI? Explains the differences between human and artificial intelligence, examines Robert Dilt’s logical levels, and connects these concepts to various types of AI. It also discusses the impact of the 4th industrial revolution.
- Managing Data for AI: Introduces basic data literacy, explains different data types, and covers the importance of data semantics and syntax. It also addresses privacy concerns and the potential insights that can be derived from data.
- Ethics, Risks, and Trustworthiness: Explores the ethical challenges and risks associated with AI, including the importance of explainable AI. It introduces EU ethical guidelines and legislation, emphasizing the need to maintain public trust in AI technologies.
- Predictions, Algorithms, Machine, and Deep Learning: Provides an overview of various levels of prediction, key algorithms, common problem types, and generative AI models. It highlights which algorithms are best suited for specific types of problems
- Building and Assessing an AI Application: Outlines a basic approach to developing a simple AI application using the CRISP-DM methodology. It emphasizes the importance of understanding the business context and assessing reliability at each step of the process.
-
Learning outcomes
Candidates should be able to demonstrate a knowledge and understanding in the following topics:
- What is AI? Explains the differences between human and artificial intelligence, examines Robert Dilt’s logical levels, and connects these concepts to various types of AI. It also discusses the impact of the 4th industrial revolution.
- Managing Data for AI: Introduces basic data literacy, explains different data types, and covers the importance of data semantics and syntax. It also addresses privacy concerns and the potential insights that can be derived from data.
- Ethics, Risks, and Trustworthiness: Explores the ethical challenges and risks associated with AI, including the importance of explainable AI. It introduces EU ethical guidelines and legislation, emphasizing the need to maintain public trust in AI technologies.
- Predictions, Algorithms, Machine, and Deep Learning: Provides an overview of various levels of prediction, key algorithms, common problem types, and generative AI models. It highlights which algorithms are best suited for specific types of problems
- Building and Assessing an AI Application: Outlines a basic approach to developing a simple AI application using the CRISP-DM methodology. It emphasizes the importance of understanding the business context and assessing reliability at each step of the process.
-
Course outlines
- Module 1: Introduction to AI Fundamentals
- Module 2: Ethical and Sustainable Human and Artificial Intelligence
- Module 3: AI Benefits, Challenges and Projects
- Module 4: Machine Learning (ML) Toolbox – Theory and Practice
- Module 5: Agile Working and The Future of Human and Machine collaboration
- Module 6: AI Fundamentals Wrap Up
- Module 7: Trial Exam
Exam Information
- 20 Multiple-choice questions
- 13 marks required to pass – 65%
- 30 minutes exam duration
- Closed book
-
Prequisites
There are no mandatory prerequisites.
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