AI Governance Professional (AIGP) Certification & AI Mastery
YouAccel Training
27:26:49
Description
Master the 7 Domains of the AIGP Certification with Expert Guidance in AI Governance and Ethical Standards
What You'll Learn?
- The distinction between narrow and general AI and how these systems operate within various industries.
- Core principles of machine learning including supervised, unsupervised, and reinforcement learning techniques.
- Advanced AI concepts such as deep learning and transformer models, with a focus on their theoretical foundations.
- Natural Language Processing (NLP) and multi-modal models, and their application in enhancing AI systems.
- The ethical and societal implications of AI, including its impact on privacy, discrimination, and public trust.
- Global AI governance frameworks, including standards from the OECD, EU, and other international bodies.
- Responsible AI principles, focusing on transparency, accountability, and human-centric design in AI systems.
- The legal and regulatory landscape for AI, covering laws related to non-discrimination, data protection, and intellectual property.
- AI development life cycle, from defining business objectives and governance structures to model testing and validation.
- Post-deployment AI system management, including monitoring, validation, and addressing automation bias.
Who is this for?
What You Need to Know?
More details
DescriptionThis course is designed to provide a deep theoretical understanding of the fundamental concepts that underpin AI and machine learning (ML) technologies, with a specific focus on preparing students for the AI Governance Professional (AIGP) Certification. Throughout the course, students will explore the 7 critical domains required for certification: AI governance and risk management, regulatory compliance, ethical AI frameworks, data privacy and protection, AI bias mitigation, human-centered AI, and responsible AI innovation. Mastery of these domains is essential for navigating the ethical, legal, and governance challenges posed by AI technologies.
Students will explore key ideas driving AI innovation, with a particular focus on understanding the various types of AI systems, including narrow and general AI. This distinction is crucial for understanding the scope and limitations of current AI technologies, as well as their potential future developments. The course also delves into machine learning basics, explaining different training methods and algorithms that form the core of intelligent systems.
As AI continues to evolve, deep learning and transformer models have become integral to advancements in the field. Students will examine these theoretical frameworks, focusing on their roles in modern AI applications, particularly in generative AI and natural language processing (NLP). Additionally, the course addresses multi-modal models, which combine various data types to enhance AI capabilities in fields such as healthcare and education. The interdisciplinary nature of AI will also be discussed, highlighting the collaboration required between technical experts and social scientists to ensure responsible AI development.
The history and evolution of AI are critical to understanding the trajectory of these technologies. The course will trace AI’s development from its early stages to its current status as a transformative tool in many industries. This historical context helps frame the ethical and social responsibilities associated with AI. A key component of the course involves discussing AI’s broader impacts on society, from individual harms such as privacy violations to group-level biases and discrimination. Students will gain insight into how AI affects democratic processes, education, and public trust, as well as the potential economic repercussions, including the redistribution of jobs and economic opportunities.
In exploring responsible AI, the course emphasizes the importance of developing trustworthy AI systems. Students will learn about the core principles of responsible AI, such as transparency, accountability, and human-centric design, which are essential for building ethical AI technologies. The course also covers privacy-enhanced AI systems, discussing the balance between data utility and privacy protection. To ensure students understand the global regulatory landscape, the course includes an overview of international standards for trustworthy AI, including frameworks established by organizations like the OECD and the EU.
A key aspect of this course is its comprehensive preparation for the AI Governance Professional (AIGP) Certification. This certification focuses on equipping professionals with the knowledge and skills to navigate the ethical, legal, and governance challenges posed by AI technologies. The AIGP Certification provides significant benefits, including enhanced credibility in AI ethics and governance, a deep understanding of global AI regulatory frameworks, and the ability to effectively manage AI risks in various industries. By earning this certification, students will be better positioned to lead organizations in implementing responsible AI practices and ensuring compliance with evolving regulations.
Another critical aspect of the course is understanding the legal and regulatory frameworks that govern AI development and deployment. Students will explore AI-specific laws and regulations, including non-discrimination laws and privacy protections that apply to AI applications. This section of the course will provide an in-depth examination of key legislative efforts worldwide, including the EU Digital Services Act and the AI-related provisions of the GDPR. By understanding these frameworks, students will gain insight into the legal considerations that must be navigated when deploying AI systems.
Finally, the course will walk students through the AI development life cycle, focusing on the theoretical aspects of planning, governance, and risk management. Students will learn how to define business objectives for AI projects, establish governance structures, and address challenges related to data strategy and model selection. Ethical considerations in AI system architecture will also be explored, emphasizing the importance of fairness, transparency, and accountability. The course concludes by discussing the post-deployment management of AI systems, including monitoring, validation, and ensuring ethical operation throughout the system's life cycle.
Overall, this course offers a comprehensive theoretical foundation in AI and machine learning, focusing on the ethical, social, and legal considerations necessary for the responsible development and deployment of AI technologies. It provides students not only with a strong understanding of AI governance and societal impacts but also prepares them to obtain the highly regarded AI Governance Professional (AIGP) Certification, enhancing their career prospects in the rapidly evolving field of AI governance.
Who this course is for:
- Aspiring AI leaders seeking comprehensive knowledge in AI governance
- AI professionals aiming to enhance their expertise in ethical AI practices
- Policy makers interested in understanding AI regulatory landscapes
- Risk management experts focusing on AI-related challenges and solutions
- Corporate strategists looking to implement effective AI governance measures
- Academics and researchers exploring the ethical and societal impacts of AI
- Public sector employees involved in AI policy development and implementation
- Individuals committed to responsible and equitable AI governance practices
This course is designed to provide a deep theoretical understanding of the fundamental concepts that underpin AI and machine learning (ML) technologies, with a specific focus on preparing students for the AI Governance Professional (AIGP) Certification. Throughout the course, students will explore the 7 critical domains required for certification: AI governance and risk management, regulatory compliance, ethical AI frameworks, data privacy and protection, AI bias mitigation, human-centered AI, and responsible AI innovation. Mastery of these domains is essential for navigating the ethical, legal, and governance challenges posed by AI technologies.
Students will explore key ideas driving AI innovation, with a particular focus on understanding the various types of AI systems, including narrow and general AI. This distinction is crucial for understanding the scope and limitations of current AI technologies, as well as their potential future developments. The course also delves into machine learning basics, explaining different training methods and algorithms that form the core of intelligent systems.
As AI continues to evolve, deep learning and transformer models have become integral to advancements in the field. Students will examine these theoretical frameworks, focusing on their roles in modern AI applications, particularly in generative AI and natural language processing (NLP). Additionally, the course addresses multi-modal models, which combine various data types to enhance AI capabilities in fields such as healthcare and education. The interdisciplinary nature of AI will also be discussed, highlighting the collaboration required between technical experts and social scientists to ensure responsible AI development.
The history and evolution of AI are critical to understanding the trajectory of these technologies. The course will trace AI’s development from its early stages to its current status as a transformative tool in many industries. This historical context helps frame the ethical and social responsibilities associated with AI. A key component of the course involves discussing AI’s broader impacts on society, from individual harms such as privacy violations to group-level biases and discrimination. Students will gain insight into how AI affects democratic processes, education, and public trust, as well as the potential economic repercussions, including the redistribution of jobs and economic opportunities.
In exploring responsible AI, the course emphasizes the importance of developing trustworthy AI systems. Students will learn about the core principles of responsible AI, such as transparency, accountability, and human-centric design, which are essential for building ethical AI technologies. The course also covers privacy-enhanced AI systems, discussing the balance between data utility and privacy protection. To ensure students understand the global regulatory landscape, the course includes an overview of international standards for trustworthy AI, including frameworks established by organizations like the OECD and the EU.
A key aspect of this course is its comprehensive preparation for the AI Governance Professional (AIGP) Certification. This certification focuses on equipping professionals with the knowledge and skills to navigate the ethical, legal, and governance challenges posed by AI technologies. The AIGP Certification provides significant benefits, including enhanced credibility in AI ethics and governance, a deep understanding of global AI regulatory frameworks, and the ability to effectively manage AI risks in various industries. By earning this certification, students will be better positioned to lead organizations in implementing responsible AI practices and ensuring compliance with evolving regulations.
Another critical aspect of the course is understanding the legal and regulatory frameworks that govern AI development and deployment. Students will explore AI-specific laws and regulations, including non-discrimination laws and privacy protections that apply to AI applications. This section of the course will provide an in-depth examination of key legislative efforts worldwide, including the EU Digital Services Act and the AI-related provisions of the GDPR. By understanding these frameworks, students will gain insight into the legal considerations that must be navigated when deploying AI systems.
Finally, the course will walk students through the AI development life cycle, focusing on the theoretical aspects of planning, governance, and risk management. Students will learn how to define business objectives for AI projects, establish governance structures, and address challenges related to data strategy and model selection. Ethical considerations in AI system architecture will also be explored, emphasizing the importance of fairness, transparency, and accountability. The course concludes by discussing the post-deployment management of AI systems, including monitoring, validation, and ensuring ethical operation throughout the system's life cycle.
Overall, this course offers a comprehensive theoretical foundation in AI and machine learning, focusing on the ethical, social, and legal considerations necessary for the responsible development and deployment of AI technologies. It provides students not only with a strong understanding of AI governance and societal impacts but also prepares them to obtain the highly regarded AI Governance Professional (AIGP) Certification, enhancing their career prospects in the rapidly evolving field of AI governance.
Who this course is for:
- Aspiring AI leaders seeking comprehensive knowledge in AI governance
- AI professionals aiming to enhance their expertise in ethical AI practices
- Policy makers interested in understanding AI regulatory landscapes
- Risk management experts focusing on AI-related challenges and solutions
- Corporate strategists looking to implement effective AI governance measures
- Academics and researchers exploring the ethical and societal impacts of AI
- Public sector employees involved in AI policy development and implementation
- Individuals committed to responsible and equitable AI governance practices
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YouAccel Training
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 240
- duration 27:26:49
- Release Date 2024/11/21
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