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Free Download Ai Governance Professional (Aigp) Certification & Ai Mastery
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.37 GB | Duration: 27h 27m
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.
Requirements
No Prerequisites.
Description
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.
Overview
Section 1: Course Resources and Downloads
Lecture 1 Course Resources and Downloads
Section 2: Foundations of AI and Machine Learning
Lecture 2 Section Introduction
Lecture 3 Introduction to AI and Machine Learning
Lecture 4 Case Study: AI-Diagnosis: Transforming Healthcare with AI and ML
Lecture 5 Types of AI Systems: Narrow vs. General AI
Lecture 6 Case Study: Navigating AI Governance
Lecture 7 Machine Learning Basics and Training Methods
Lecture 8 Case Study: Enhancing Customer Churn Prediction
Lecture 9 Deep Learning, Generative AI, and Transformer Models
Lecture 10 Case Study: Transformative AI: Integrating Deep Learning
Lecture 11 Natural Language Processing and Multi-modal Models
Lecture 12 Case Study: Revolutionizing Healthcare and Education with NLP and Multi-Modal AI
Lecture 13 Socio-technical AI Systems and Cross-disciplinary Collaboration
Lecture 14 Case Study: Integrating Technical Excellence and Social Responsibility
Lecture 15 The History and Evolution of AI and Data Science
Lecture 16 Case Study: Bridging AI's Past and Present
Lecture 17 Section Summary
Section 3: Understanding AI Impacts on Society
Lecture 18 Section Introduction
Lecture 19 Individual Harms: Civil Rights, Safety, and Economic Impact
Lecture 20 Case Study: Navigating AI's Challenges
Lecture 21 Group Harms: Discrimination and Bias in AI Systems
Lecture 22 Case Study: Addressing AI Bias
Lecture 23 Societal Harms: Democracy, Education, and Public Trust
Lecture 24 Case Study: AI's Impact on Democracy, Education, and Public Trust
Lecture 25 Organizational Risks: Reputational, Cultural, and Economic Threats
Lecture 26 Case Study: Navigating AI Governance
Lecture 27 Environmental and Ecosystem Impacts of AI
Lecture 28 Case Study: Balancing AI Progress with Sustainability
Lecture 29 Redistribution of Jobs and Economic Opportunities Due to AI
Lecture 30 Case Study: Balancing AI Integration and Workforce Reskilling
Lecture 31 AI's Impact on Workforce and Educational Access
Lecture 32 Case Study: TechNova's Strategic Approach to Workforce Reskilling
Lecture 33 Section Summary
Section 4: Responsible AI Principles and Trustworthy AI
Lecture 34 Section Introduction
Lecture 35 Core Principles of Responsible AI
Lecture 36 Case Study: Building Ethical AI
Lecture 37 Human-centric AI Systems
Lecture 38 Case Study: Human-Centric AI for Urban Traffic Management
Lecture 39 Transparency, Explainability, and Accountability in AI
Lecture 40 Case Study: Balancing Innovation and Ethics
Lecture 41 Safe, Secure, and Resilient AI Systems
Lecture 42 Case Study: Ensuring Ethical, Secure, and Resilient AI
Lecture 43 Privacy-Enhanced AI Systems and Data Protection
Lecture 44 Case Study: Balancing Data Utility and Privacy in AI
Lecture 45 OECD and EU Standards for Trustworthy AI
Lecture 46 Case Study: Navigating Ethical Challenges in AI-Driven Healthcare Innovation
Lecture 47 Comparison of Global Ethical Guidelines for AI
Lecture 48 Case Study: Navigating Global Ethical Standards for AI
Lecture 49 Section Summary
Section 5: AI Laws and Regulatory Compliance
Lecture 50 Section Introduction
Lecture 51 Overview of AI-Specific Laws and Regulations
Lecture 52 Case Study: Navigating Global AI Regulations
Lecture 53 Non-Discrimination Laws and AI Applications
Lecture 54 Case Study: Mitigating AI Bias: DiversiHire's Journey Through Fairness
Lecture 55 Product Safety Laws for AI Systems
Lecture 56 Case Study: Ensuring AI Safety
Lecture 57 Privacy and Data Protection in AI Systems
Lecture 58 Case Study: Balancing AI Innovation with Privacy and Ethics
Lecture 59 Intellectual Property and AI: Legal Considerations
Lecture 60 Case Study: Navigating AI and IP Law
Lecture 61 Key Components of the EU Digital Services Act
Lecture 62 Case Study: Navigating DSA Compliance
Lecture 63 The Intersection of AI and GDPR Requirements
Lecture 64 Case Study: Balancing AI Innovation and GDPR Compliance
Lecture 65 Section Summary
Section 6: Global AI Legal Frameworks
Lecture 66 Section Introduction
Lecture 67 Overview of the EU AI Act and Its Risk Categories
Lecture 68 Case Study: Implementing the EU AI Act
Lecture 69 Requirements for High-Risk AI Systems and Foundation Models
Lecture 70 Case Study: Ensuring Ethical and Effective Deployment of High-Risk AI
Lecture 71 Notification and Enforcement Mechanisms under the EU AI Act
Lecture 72 Case Study: TechNova's Strategic Response to EU AI Act Compliance Challenges
Lecture 73 Canada's Artificial Intelligence and Data Act (Bill C-27)
Lecture 74 Case Study: Balancing AI Innovation and Ethical Governance
Lecture 75 Key Components of U.S. AI-related State Laws
Lecture 76 Case Study: Navigating AI Regulations
Lecture 77 China's Draft Regulations on Generative AI
Lecture 78 Case Study: Navigating China's AI Regulations
Lecture 79 Harmonizing Global AI Laws and Risk Management Frameworks
Lecture 80 Case Study: Harmonizing Global AI Laws
Lecture 81 Section Summary
Section 7: AI Development Life Cycle - Planning
Lecture 82 Section Introduction
Lecture 83 Defining Business Objectives and AI System Scope
Lecture 84 Case Study: Optimizing Customer Service with AI
Lecture 85 Determining AI Governance Structures and Responsibilities
Lecture 86 Case Study: Ethical AI Governance
Lecture 87 Data Strategy: Collection, Labeling, and Cleaning
Lecture 88 Case Study: TechNova's AI Chatbot Success
Lecture 89 Model Selection: Accuracy vs. Interpretability
Lecture 90 Case Study: Balancing Accuracy and Interpretability in AI
Lecture 91 Ethical Design in AI System Architecture
Lecture 92 Case Study: FairAI's Commitment to Fairness, Transparency, and Accountability
Lecture 93 Understanding the Governance Challenges in AI Planning
Lecture 94 Case Study: Governance Challenges in AI Planning
Lecture 95 Cross-functional Team Collaboration in AI Planning
Lecture 96 Case Study: Cross-Functional Synergy
Lecture 97 Section Summary
Section 8: AI Development Life Cycle - Development and Testing
Lecture 98 Section Introduction
Lecture 99 Feature Engineering for AI Models
Lecture 100 Case Study: Enhancing Predictive Health Analytics
Lecture 101 Model Training: Techniques and Best Practices
Lecture 102 Case Study: Optimizing AI for Rare Disease Detection
Lecture 103 Model Testing and Validation Processes
Lecture 104 Case Study: Rigorous Testing and Ethical Considerations
Lecture 105 Testing AI Models with Edge Cases and Adversarial Inputs
Lecture 106 Case Study: Ensuring Robustness and Reliability in Autonomous Drone AI
Lecture 107 Privacy-preserving Machine Learning Techniques
Lecture 108 Case Study: Balancing Privacy and Utility
Lecture 109 Repeatability Assessments and Model Fact Sheets
Lecture 110 Case Study: Ensuring AI Model Reliability and Transparency
Lecture 111 Conducting Algorithm Impact Assessments
Lecture 112 Case Study: Ensuring Fairness and Accountability
Lecture 113 Section Summary
Section 9: Implementing AI Governance and Risk Management
Lecture 114 Section Introduction
Lecture 115 Creating AI Risk Management Frameworks
Lecture 116 Case Study: Comprehensive AI Risk Management
Lecture 117 AI Governance Infrastructure: Key Roles and Responsibilities
Lecture 118 Case Study: Comprehensive AI Governance
Lecture 119 Cross-functional Collaboration in AI Governance
Lecture 120 Case Study: Cross-Functional Collaboration
Lecture 121 AI Regulatory Requirements and Compliance Procedures
Lecture 122 Case Study: TechNova's Path to Ethical and Compliant AI
Lecture 123 Establishing a Responsible AI Culture within Organizations
Lecture 124 Case Study: Establishing Responsible AI
Lecture 125 Assessing AI Maturity Levels in Business Functions
Lecture 126 Case Study: Enhancing AI Maturity
Lecture 127 Managing Third-Party Risks in AI Systems
Lecture 128 Case Study: Managing Third-Party Risks in AI
Lecture 129 Section Summary
Section 10: AI Project Management and Risk Analysis
Lecture 130 Section Introduction
Lecture 131 Scoping AI Projects: Identifying Key Objectives
Lecture 132 Case Study: Strategic Scoping of AI Projects
Lecture 133 Mapping AI Risks: Identifying Internal and External Threats
Lecture 134 Case Study: Overcoming Challenges in Developing an AI-Driven Recruitment Tool
Lecture 135 Developing Risk Mitigation Strategies for AI Projects
Lecture 136 Case Study: Comprehensive Risk Management Strategies for Successful AI Projects
Lecture 137 Constructing a Harms Matrix for AI Risk Assessment
Lecture 138 Case Study: Harms Matrix: Mitigating Risks in AI-Driven Cancer Diagnostics
Lecture 139 Conducting Algorithm Impact Assessments
Lecture 140 Case Study: TechNova's AI Hiring Algorithm
Lecture 141 Engaging Stakeholders in AI Risk Management
Lecture 142 Case Study: Ensuring Ethical AI
Lecture 143 Data Provenance, Lineage, and Accuracy in AI Systems
Lecture 144 Case Study: Ensuring Data Integrity and Transparency in AI Systems
Lecture 145 Section Summary
Section 11: Post-Deployment AI System Management
Lecture 146 Section Introduction
Lecture 147 Continuous Monitoring and Validation of AI Systems
Lecture 148 Case Study: Continuous Monitoring and Ethical Oversight
Lecture 149 Post-Hoc Testing for AI System Accuracy and Effectiveness
Lecture 150 Case Study: Ensuring AI Tool Accuracy, Fairness, and Robustness
Lecture 151 Managing Automation Bias in AI Systems
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