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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 Lecture 152 Case Study: Balancing AI and Clinical Judgment Lecture 153 Model Versioning and Updates: Best Practices Lecture 154 Case Study: Structured AI Model Versioning Lecture 155 Managing Third-Party Risks Post-Deployment Lecture 156 Case Study: Managing Third-Party Risks Lecture 157 Reducing Unintended Use and Downstream Harm in AI Systems Lecture 158 Case Study: Ethical Governance and Transparency in AI-Driven Healthcare Lecture 159 Planning for AI System Deactivation and System Sunset Lecture 160 Case Study: Effective Strategies for AI System Deactivation Lecture 161 Section Summary Section 12: AI Ethics and Accountability Lecture 162 Section Introduction Lecture 163 Building a Global AI Auditing Framework Lecture 164 Case Study: Global AI Auditing Framework Lecture 165 Establishing AI Auditing Standards and Compliance Measures Lecture 166 Case Study: Implementing Ethical AI Auditing Lecture 167 Accountability in Automated Decision-Making Systems Lecture 168 Case Study: Ensuring Accountability and Fairness in AI-Driven Loan Approval Lecture 169 Enhancing AI Governance with Automated Compliance Tools Lecture 170 Case Study: Enhancing AI Governance Lecture 171 Ethical Dilemmas in AI Governance and Deployment Lecture 172 Case Study: Navigating Ethical Challenges in AI Deployment Lecture 173 Understanding AI Failures: Bias, Hallucinations, and Errors Lecture 174 Case Study: Mitigating AI Bias, Hallucinations, and Errors Lecture 175 Managing Cultural and Behavioral Change in AI Teams Lecture 176 Case Study: TechNova's Journey in Managing Cultural and Behavioral Change Lecture 177 Section Summary Section 13: Emerging AI Technologies and Future Trends Lecture 178 Section Introduction Lecture 179 Advances in Generative AI and Multi-modal AI Models Lecture 180 Case Study: Revolutionizing Healthcare with Generative and Multi-Modal AI Lecture 181 Natural Language Processing (NLP) and Large Language Models Lecture 182 Case Study: Revolutionizing Customer Support with NLP and LLMs Lecture 183 AI in Robotics, Automation, and Autonomous Systems Lecture 184 Case Study: AI-Driven Innovations Lecture 185 AI's Role in the Metaverse, AR, and VR Lecture 186 Case Study: Integrating AI in the Metaverse Lecture 187 Emerging Trends in AI for Healthcare and Medicine Lecture 188 Case Study: AI Revolutionizing Healthcare Lecture 189 AI in Environmental and Sustainability Applications Lecture 190 Case Study: AI-Powered Sustainability Lecture 191 Predicting the Future of AI: Trends and Challenges Lecture 192 Case Study: AI in Healthcare: Balancing Innovation, Ethics, and Governance Lecture 193 Section Summary Section 14: AI in the Socio-Cultural Context Lecture 194 Section Introduction Lecture 195 AI's Impact on Jobs and Employment Opportunities Lecture 196 Case Study: Transforming Employment Lecture 197 The Redistribution of Wealth and Economic Power via AI Lecture 198 Case Study: Navigating Inequality, Market Shifts, and Regulatory Challenges Lecture 199 AI's Influence on Education and Lifelong Learning Lecture 200 Case Study: Personalized Learning, Efficiency, and Inclusivity at Westbrook High Lecture 201 Public Trust in AI and Its Governance Lecture 202 Case Study: The HealthAI Case Study on Governance and Ethical Integration Lecture 203 AI and Democratic Processes: Challenges and Opportunities Lecture 204 Case Study: AI's Impact on Democracy Lecture 205 Building Inclusive AI Systems for Diverse Societies Lecture 206 Case Study: TechNova's Journey to Equitable Job Recruitment Systems Lecture 207 Case Study: Strategic Innovation and Adaptability Lecture 208 Section Summary Section 15: AI Auditing, Evaluation, and Impact Measurement Lecture 209 Section Introduction Lecture 210 Methods and Tools for Conducting AI Audits Lecture 211 Case Study: Comprehensive AI Audit at TechNova Lecture 212 Evaluating AI's Societal Impact: Metrics and Approaches Lecture 213 Case Study: Evaluating AI's Societal Impact Lecture 214 Tracking AI System Performance Post-Deployment Lecture 215 Case Study: Optimizing AI Post-Deployment Lecture 216 Remediating AI System Failures and Negative Impacts Lecture 217 Case Study: Enhancing AI Governance Lecture 218 Reporting and Communicating AI System Risks Lecture 219 Case Study: Ensuring AI Integrity Lecture 220 Creating Ethical AI Impact Reports for Stakeholders Lecture 221 Case Study: Transparency, Fairness, Privacy, Accountability, and Societal Impact Lecture 222 Preparing AI Systems for Continuous Evaluation and Updates Lecture 223 Case Study: Continuous Improvement and Reliability Lecture 224 Section Summary Section 16: Contemplating Ongoing AI Issues and Challenges Lecture 225 Section Introduction Lecture 226 Legal Challenges of AI: Tort Liability and Responsibility Lecture 227 Case Study: AI Liability in Autonomous Vehicle Accidents Lecture 228 Intellectual Property Rights and AI System Ownership Lecture 229 Case Study: AI-Generated Art and Intellectual Property Lecture 230 Educating Users on the Functions and Limitations of AI Lecture 231 Case Study: Harnessing AI Responsibly Lecture 232 Addressing Workforce Upskilling and Reskilling Needs Lecture 233 Case Study: Navigating AI-Driven Workforce Transformation Lecture 234 Building a Profession of AI Auditors: Standards and Training Lecture 235 Case Study: Ensuring Ethical and Fair AI Lecture 236 Automated Governance for AI Ethical Issues Lecture 237 Case Study: Ethical AI Governance Lecture 238 Preparing for the Future of AI Governance and Ethics Lecture 239 Case Study: Navigating Ethical AI Governance Lecture 240 Section Summary Section 17: Course Summary Lecture 241 Conclusion 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 Homepage https://www.udemy.com/course/ai-governance-professional-aigp-certification-ai-mastery/ TakeFile https://takefile.link/f7sakrphedgh/ufxbk.Ai.Governance.Professional.Aigp.Certification..Ai.Mastery.part1.rar.html https://takefile.link/6xvfxcx6mr7k/ufxbk.Ai.Governance.Professional.Aigp.Certification..Ai.Mastery.part2.rar.html https://takefile.link/vjlcmcrme70n/ufxbk.Ai.Governance.Professional.Aigp.Certification..Ai.Mastery.part3.rar.html 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