Course Information
Course Overview
Complete AI Guide: Machine Learning, Ethics, Governance & Career Opportunities for All
This course offers a comprehensive introduction to Artificial Intelligence (AI) fundamentals, specifically designed to align with ISACA’s AI Fundamentals Certificate. It equips learners with essential knowledge, ethical considerations, and practical frameworks to responsibly understand and apply AI in professional environments. Whether you are entering the world of AI or looking to strengthen your foundation for auditing, governance, or risk roles, this course provides actionable insights and exam-aligned content.
The course explores the following key topics:
Core AI Concepts and Terminology, including machine learning, neural networks, and natural language processing.
AI Capabilities and Applications, showcasing real-world use cases across industries.
AI Lifecycle and Model Management, covering stages like data acquisition, training, validation, and deployment.
AI Ethics and Responsible Use, emphasizing fairness, transparency, and compliance with emerging standards.
Regulatory and Legal Considerations, exploring laws, frameworks, and compliance practices relevant to AI systems.
By the end of this course, learners will be able to:
Understand foundational AI concepts and terminology.
Recognize AI use cases and how they apply in business and IT environments.
Identify and mitigate risks associated with AI systems.
Apply principles of responsible AI and ethical decision-making.
Align AI practices with governance, regulatory, and legal frameworks.
Through expert instruction, real-world examples, and hands-on guidance, this course empowers professionals to build AI literacy and become responsible stewards of AI in the digital age.
Course Content
- 24 section(s)
- 74 lecture(s)
- Section 1 Understanding AI Governance
- Section 2 Understanding AI Risk Management
- Section 3 The Intersection of Governance and Risk Management
- Section 4 Role in Responsible AI
- Section 5 Global AI Governance Frameworks
- Section 6 Stakeholder Roles and Responsibilities
- Section 7 Multi-Level Governance Structures
- Section 8 AI Risk Taxonomy and Categories
- Section 9 AI Risk Assessment Methodologies
- Section 10 AI Risk Registers and Documentation
- Section 11 AI/ML Lifecycle Governance Integration
- Section 12 Documentation and Policy Requirements
- Section 13 Internal Controls and Assurance
- Section 14 Generative AI and LLM-Specific Controls
- Section 15 Global Regulatory Landscape
- Section 16 Compliance Strategy and Implementation
- Section 17 Legal Risk Management
- Section 18 AI Ethics Framework and Principles
- Section 19 Bias Detection and Mitigation
- Section 20 Transparency and Explainability
- Section 21 Human Oversight and Control
- Section 22 AI Risk Strategy and Resilience Planning
- Section 23 AI Incident Response Framework
- Section 24 Monitoring and Continuous Improvement
What You’ll Learn
- Grasp essential AI concepts, terminology, and the different types of AI technologies, including machine learning and neural networks., Understand real-world AI use cases and how AI is transforming industries and organizational functions., Identify and manage ethical, governance, and risk-related concerns associated with AI systems., Prepare confidently for AI Certifications with structured content
Skills covered in this course
Reviews
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VVishal
good explanation of concepts