Understanding Generative AI for Tech Leaders

Courses under this program:
Course 1: What Is Generative AI?
-Learn about the basics of generative AI, including its history, popular models, how it works, ethical implications, and much more.

Course 2: Generative AI vs. Traditional AI
-See the key differences between generative and traditional AI.

Course 3: Generative AI: Introduction to Large Language Models
-Gain a foundational knowledge of how large language models and other Generative AI models work.

Course 4: Introduction to NLP and LLMs: Principles and Practical Applications
-Gain a practical understanding of natural language processing (NLP) and large language models (LLMs), unlocking the exciting possibilities of these transformative technologies.

Course 5: Ethics in the Age of Generative AI
-Learn why ethical considerations are a critical part of the generative AI creation and deployment process and explore ways to address these ethical challenges.

Course 6: Building a Responsible AI Program: Context, Culture, Content, and Commitment
-Organizations who use AI need to ensure they do so in a socially responsible way. Learn how in this course.

Course 7: Generative AI Approaches to Business Challenges
-Learn how to use Generative AI to solve real-world business problems in task automation, customer personalization, content creation, data analysis, and knowledge management.

Course 8: Exploring AIOps
-Discover how the exciting new world of AIOps is changing everyday IT workflows and practices around the world.

Course 9: Understanding Generative AI in Cloud Computing: Services and Use Cases
-Learn how to get started using cloud-based generative AI services and tools.

Course 10: Building in Azure AI Foundry
-Get started with Azure AI Foundry, the unified platform for managing, building, evaluating, and deploying AI applications from Microsoft.

Course 11: Azure AI Engineering: Speech, Language, and Vision Solutions
-Learn how to build and implement AI-powered solutions using Microsoft Azure services.

Course 12: Understanding and Implementing the NIST AI Risk Management Framework (RMF)
-Learn how to implement and maintain the core principles of the NIST AI Risk Management Framework (RMF) within a business.

Course 13: ISO/IEC 42001:2023: Understanding and Implementing the Artificial Intelligence Management System (AIMS) Standard
-Get a comprehensive introduction to the ISO/IEC 42001 Artificial Intelligence Management System (AIMS) standard.

Course 14: Introduction to Auditing AI Systems
-Learn the fundamentals of how to adapt to and comply with AI regulation in this introductory course on auditing AI systems for bias and discrimination.

Course 15: Introduction to AI Governance
-Explore core concepts and practical strategies to effectively implement and manage AI governance.

Course 16: Compliance and Regulations for Generative AI
-Learn about compliance building blocks and considerations that you should keep in mind when adopting generative AI.

Course 17: AI and the Future of Work: Workflows and Modern Tools for Tech Leaders
-Learn how to implement machine learning operations into your business, discover the scope of what ML can achieve—and the challenges that it can present.

Course 18: Scaling Generative AI: Building a Strategy for Adoption and Expansion
-Discover how to leverage OpenAI tools within your organization and effectively apply and scale AI technologies while ensuring compliance, managing risks, and achieving innovation.

Course 19: Building a Generative AI Tech Strategy
-Learn how to create a tech strategy that leverages generative AI.

Course 20: RAG and Fine-Tuning Explained
-This course breaks down the AI concepts Retrieval Augmented Generation (RAG) and fine-tuning to explain how they factor into building robust enterprise applications.

Course 21: Introduction to Multimodal Prompting for Generative AI
-Learn how you can leverage modern AI systems that utilize multimodality.

Course 22: Agentic AI: Building Data-First AI Agents
-Explore the biggest data challenges organizations face today when implementing AI, including data quality, privacy, integration, and bias, and learn strategies to overcome them.