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Free Download AI-Driven Supply Chain - Mastering ChatGPT for Efficiency Published 9/2024 Created by Amanda Bennetts MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 21 Lectures ( 2h 19m ) | Size: 3.1 GB Explore how ChatGPT can enhance supply chain components and lead to smarter, faster decision-making. What you'll learn: Leverage AI to optimize demand forecasting and mitigate supply chain risks using real-time data analysis and scenario planning. Automate supplier communication and streamline contract management through ChatGPT's natural language processing capabilities. Improve logistics and route optimization by applying AI insights to reduce transportation costs and ensure timely deliveries. Implement AI-driven warehouse automation and enhance order fulfillment accuracy to increase operational efficiency and customer satisfaction. Requirements: Basic understanding of supply chain management concepts - Familiarity with supply chain operations, including logistics, warehousing, and supplier relationships, is recommended. Comfortable with using digital tools and technologies - While no advanced technical skills are required, a basic knowledge of AI applications and digital tools will be helpful. A mindset for innovation and improvement - This course is designed for learners who are eager to explore new technologies like AI to improve operational efficiency and decision-making. Description: Are you ready to revolutionize your supply chain and stay ahead in today's fast-paced business world? Whether you're a supply chain manager, operations executive, or aspiring leader, this course will provide you with the tools and knowledge to transform how your organization manages logistics, inventory, suppliers, and distribution using AI-driven solutions.In this comprehensive course, *AI-Driven Supply Chain: Mastering ChatGPT for Efficiency*, you'll discover how ChatGPT and cutting-edge AI technologies can streamline your supply chain, reduce costs, and make faster, smarter decisions. With real-world examples, case studies, and practical applications, you'll gain actionable insights that will help you lead your organization to the next level of operational excellence.### **Key Learning Outcomes:**1. **Transform Supply Chain Planning with AI:**Discover how ChatGPT can improve demand forecasting, scenario analysis, and risk mitigation. Learn how AI can analyze data from various sources, predict demand shifts, and help you prepare for potential disruptions, giving you a competitive edge in managing inventory and avoiding costly missteps.2. **Streamline Supplier Communication and Contract Management:**Communication with suppliers can be time-consuming, but ChatGPT simplifies this process by automating routine inquiries, tracking shipments, and even managing contracts. You'll learn how AI can improve relationships with suppliers, enhance transparency, and reduce the risk of human error, ensuring smoother collaboration.3. **Optimize Logistics, Routes, and Delivery Scheduling:**Master how ChatGPT can analyze real-time data to optimize your logistics network, from delivery route planning to cost management. Learn how AI helps you dynamically adjust delivery schedules, reduce transportation costs, and ensure on-time shipments, leading to increased customer satisfaction.4. **Automate Warehouse Operations and Improve Order Fulfillment:**Discover how AI can revolutionize warehouse automation, including inventory tracking, order picking, and packing. With ChatGPT, you'll streamline order fulfillment processes, reduce errors, and improve efficiency, all while ensuring that products reach customers quickly and accurately.5. **Ethical and Responsible AI Use:**AI is powerful, but it must be used responsibly. In this course, you'll explore the ethical considerations of AI in supply chains, from avoiding bias in decision-making to maintaining transparency and accountability. Learn how to implement AI solutions that align with your organization's sustainability and social responsibility goals.### **Why This Course is for You:**Whether you're new to AI or looking to deepen your expertise, this course is designed to suit learners of all levels. You'll get practical tools and strategies that you can immediately apply to your business, plus expert insights into how leading companies like Amazon, Walmart, and Coca-Cola use AI to drive operational efficiency.### **Take Your Supply Chain to the Next Level**With AI reshaping industries around the world, now is the time to harness its power in your supply chain. By enrolling in this course, you'll gain a solid understanding of how to integrate ChatGPT into your operations and unlock the potential of AI to create a more efficient, agile, and future-ready supply chain.Ready to master AI-driven supply chains? Enroll now and take the first step toward transforming your organization's operations. 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Free Download AI Sales Prospecting - Automate Outreach at Scale with AI Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 4h 29m | Size: 4.67 GB Use AI to Build Accurate Lead Lists, Enrich Prospects, Automate Personalize Outreach with AI Signals and Maximize Sales What you'll learn Generate hyper-targeted lead lists in minutes. Enrich prospect data with valuable insights. Craft hyper-personalized messaging for each prospect. Automate multidimensional outreach campaigns. Optimize sales outreach with AI-powered lead scoring. Use real-time signals to identify high-potential leads. Automate personalized email generation using AI. Utilize AI to track job changes and company growth. Enhance lead nurturing with AI-driven insights. Develop inbound-led outbound marketing strategies. Requirements No prior experience needed; all necessary tools and techniques will be covered. An insatiable appetite for innovation! Description Unlock The Power of AI to Supercharge your Sales Prospecting and Skyrocket your Outreach Results!In AI Sales Prospecting: Find, Enrich, Personalize Outbound with AI, you'll learn how to leverage cutting-edge AI technology to build laser-focused lead lists, enrich prospect data with valuable insights, and personalize your outreach at scale for maximum impact. You'll discover how to prospect efficiently using AI-driven messaging, people and company insights, and verified contact information to boost your lead generation and close more deals.Watch the FREE PREVIEW lessons to get a SNEAK PEEK!The Problem:In today's challenging macro-environment, it is more important than ever for sales teams to focus their efforts on the most promising prospects and close deals efficiently. However, data is often spread across various touchpoints-CRMs, outreach platforms, and more-causing teams to miss out on ripe opportunities. 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Free Download Eoghan Leahy, John Keating (Narrator), "AI-Powered Bitcoin Trading: Developing an Investment Strategy with Artificial Intelligence" English | ASIN: B0D612R874 | 2024 | MP3@64 kbps | ~04:41:00 | 129 MB Survive and thrive amongst the professional traders using sophisticated cryptocurrency analysis and trading techniques. The purpose of this book is to provide a concise yet comprehensive background of some effective methods for analyzing markets and creating fully automated AI-optimized trading systems. The book outlines some easy-to-replicate yet highly effective quant trading techniques that can be used for analyzing asset prices and then apply them to Bitcoin prices, showing how to generate actionable insights from data that can be used to create fully automated trading signals and systems. Big data analytics can be enhanced with artificial intelligence techniques. Back testing and optimization methods are presented with a special emphasis placed on the use of distributed genetic algorithms for parameter optimization. Finally, a case study of a fully automated trend-following trading strategy that leverages artificial intelligence is presented. Bitcoin AlphaBot combines human insight with AI-driven optimization to build profit table trend trading strategies. Rapidgator https://rg.to/file/f86c9bb72b2cdcd66514db659e13a72b/hx7w7.rar.html Fikper Free Download https://fikper.com/zrsksl4rmT/hx7w7.rar.html Links are Interchangeable - No Password - Single Extraction

Free Download AI and the Future of Banking by Tony Boobier, Walter Dixon, Gildan Media English | August 25, 2020 | ISBN: B08FXT8SHK | 10 hours and 48 minutes | RAR | 593 Mb An industry-specific guide to the applications of advanced analytics and AI to the banking industry. Artificial intelligence (AI) technologies help organizations to get smarter and more effective over time - ultimately responding to, learning from, and interacting with human voices. It is predicted that by 2025, half of all businesses will be using these intelligent, self-learning systems. Across its entire breadth and depth, the banking industry is at the forefront of investigating advanced analytics and AI technology for use in a broad range of applications, such as customer analytics and providing wealth advice for clients. AI and the Future of Banking provides new and established banking industry professionals with essential information on the implications of data and analytics on their roles, responsibilities, and personal career development. This accessible guide is designed to be easily understood by any banking professional with limited or no IT background. Chapters focus on practical guidance on the use of analytics to improve operational effectiveness, customer retention, and finance and risk management. Theory and published case studies are clearly explained, while considerations such as operating costs, regulation, and market saturation are discussed in real-world context. Rapidgator https://rg.to/file/6cfb6b46f5c58604850ad8b8039d03e6/gzceo.rar.html Fikper Free Download https://fikper.com/2cSGdPIv2W/gzceo.rar.html Links are Interchangeable - No Password - Single Extraction

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Free Download AI Marketing Playbook - Brand, Promote, Succeed Published 9/2024 Created by Dazz G MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 9 Lectures ( 2h 33m ) | Size: 1.41 GB Leverage AI to Build a Powerful Brand, Drive Customer Engagement, and Achieve Marketing Success What you'll learn: Effortlessly generate high-quality marketing content using AI-driven tools. Boost customer engagement with AI-powered techniques and personalized communication strategies. Create and launch a brand using branding essentials and real-world examples from print-on-demand businesses. Build and scale a personal brand with proven strategies, supported by AI for enhanced online presence. Requirements: No prior AI knowledge required. Everything you need will be explained in the course. Basic understanding of marketing concepts is helpful, but not essential. Access to a computer and internet for AI tools and course materials. Description: Discover how AI can transform your marketing strategies and elevate your brand with the AI Marketing Playbook. In this course, you'll learn to harness the power of cutting-edge AI tools like our Top-Rated Marketing Expert GPT to effortlessly create high-quality content, build a distinctive brand, and promote your business effectively.By the end of this course, you'll be equipped to generate impactful content, improve customer engagement through personalized AI campaigns, and establish a thriving brand. Whether you're a business owner, aspiring entrepreneur, or marketing professional, you'll gain the knowledge and tools to thrive in today's AI-driven landscape.Unlock the full potential of AI to supercharge your marketing efforts and build an unstoppable brand. With Marketing Expert GPT, you can streamline your workflow by engaging in intuitive conversations that gather the data and insights you need to create exceptional content with ease.Master the art of high-impact content creation and develop strategic content roadmaps that align with your business goals. Our comprehensive guide will also help you build compelling brand identities that stand out in the marketplace.Achieve higher search engine rankings and effortlessly boost customer engagement, transforming your content strategy into a powerful asset for long-term brand growth.What You'll Learn:Effortlessly create engaging, high-quality content using AI.Develop a strategic marketing roadmap to grow your business.Build a memorable, consistent brand voice powered by AI.Drive higher customer engagement and loyalty with personalized AI.Learn how to launch and promote a print-on-demand business with ease.Elevate your personal brand and stay competitive in the digital age. Who this course is for: Aspiring marketers and entrepreneurs looking to leverage AI for brand growth. Experienced marketing professionals wanting to integrate AI-driven strategies into their toolset. Business owners exploring new ways to improve customer engagement and streamline content creation. Creators or influencers aiming to elevate their personal branding through AI-driven tools. Homepage https://www.udemy.com/course/ai-marketing-playbook/ Rapidgator https://rg.to/file/51cab36f1b23974712f8104e215d6111/zfize.AI.Marketing.Playbook.Brand.Promote.Succeed.part2.rar.html https://rg.to/file/c54c2e3ce9a720f7a898ee73290e9c1f/zfize.AI.Marketing.Playbook.Brand.Promote.Succeed.part1.rar.html Fikper Free Download https://fikper.com/j6gSxHKhtt/zfize.AI.Marketing.Playbook.Brand.Promote.Succeed.part1.rar.html https://fikper.com/xfxUo8sP3c/zfize.AI.Marketing.Playbook.Brand.Promote.Succeed.part2.rar.html No Password - Links are Interchangeable

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 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Free Download AI-Enhanced Coding with Amazon Q Developer Published 9/2024 Duration: 3h 22m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 837 MB Genre: eLearning | Language: English The Sneak Peek program provides early access to Pearson video products and is exclusively available to subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. https://www.oreilly.com/videos/ai-enhanced-coding-with/9780135392812/ TakeFile https://takefile.link/fatismxn0deg/jnatk.AIEnhanced.Coding.with.Amazon.Q.Developer.rar.html Rapidgator https://rg.to/file/86f35659c011f6a90da4212e16b24d93/jnatk.AIEnhanced.Coding.with.Amazon.Q.Developer.rar.html No Password - Links are Interchangeable

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Free Download AI vs. ML vs. DL vs. Generative AI - Master Basic Concepts Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 55m | Size: 686 MB Understanding Artificial Intelligence (AI), ML, DL, Generative AI: Key Concepts, Terminology, and Real-World Use Cases What you'll learn Learners will be able to clearly differentiate between these core technologies: Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI. Learners will explore real-world examples of how AI, ML, DL, and Generative AI are applied across various industries. Learners will understand the fundamental ideas behind AI, ML, DL, and Generative AI in an easy-to-grasp way. Learners will develop the ability to choose the right tool (AI, ML, DL, or Generative AI) based on the problem at hand. Requirements No prior experience with AI or programming required - This course is designed for beginners with an interest in AI, ML, DL, and Generative AI. A basic understanding of technology and curiosity to learn - Ideal for learners who have a general interest in how modern technology works. An internet-connected device - Learners will need access to a computer or mobile device. Willingness to explore new concepts - A mindset open to learning about emerging technologies. Description Hey, friends! Ever been curious about buzzwords like Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI, and how they connect to each other? These concepts are reshaping the future, but how do they actually connect and impact the world around us? Well, you're in the right place! I've dived deep into a sea of information, sifted through endless data, and brought it all together into a simple, high-level guide. You don't need a tech degree to grasp these exciting ideas-I've distilled them into bite-sized, easy-to-understand pieces. Whether you're a tech lover, a curious learner, or just someone intrigued by the amazing power of AI, this journey is packed with insights that will get you thinking, inspired, and ready to dive deeper!This isn't just another dull tech talk-my AI assistant Spark will sprinkle (or should I say sparkle) in some humor along the way! Get ready for an engaging journey that opens the door to understanding how AI is transforming industries, sparking creativity, and shaping everyday life. We'll explore real-world applications, industry trends, and future possibilities together. Buckle up for an exciting ride!By the end of this journey, you'll have a clear grasp of these technologies and how they're driving innovation. Let's dive in! Who this course is for Beginners curious about AI, ML, DL, and Generative AI - People with little to no prior knowledge who want to learn the basic concepts. Students and professionals exploring careers in technology - Individuals who are considering a career in tech or data science and want foundational knowledge. Business leaders and decision-makers - Professionals seeking to understand these technologies for strategic purposes in their industry. Anyone interested in the future of AI and emerging technologies - Lifelong learners who want to stay updated on the latest advancements in AI. Homepage https://www.udemy.com/course/ai-master-basic-concepts/ Rapidgator https://rg.to/file/ca6489c2e6588ea434a08038f9aedab3/kmhim.AI.vs..ML.vs..DL.vs..Generative.AI.Master.Basic.Concepts.rar.html Fikper Free Download https://fikper.com/rc5x4mOINf/kmhim.AI.vs..ML.vs..DL.vs..Generative.AI.Master.Basic.Concepts.rar.html No Password - Links are Interchangeable

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