Introduction to Generative AI Learning Path Specialization Course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This specialization offers a concise and practical introduction to generative AI, large language models, and responsible AI practices. Designed by Google Cloud experts, it's ideal for professionals seeking to understand and apply generative AI responsibly in their work. The program is self-paced, with approximately 3 hours of total content, and includes hands-on insights using Google Cloud tools. No prior AI experience is required.

Module 1: Introduction to Generative AI

Estimated time: 1 hour

  • Define generative AI and its core capabilities
  • Understand how generative AI differs from traditional AI
  • Explore real-world applications of generative AI
  • Identify key use cases across industries

Module 2: Introduction to Large Language Models

Estimated time: 0.9 hours

  • Understand what large language models (LLMs) are
  • Explore common LLM use cases
  • Learn the basics of prompt tuning
  • Practice effective prompting techniques

Module 3: Introduction to Responsible AI

Estimated time: 0.3 hours

  • Define responsible AI and its importance
  • Identify ethical considerations in AI development
  • Understand fairness, accountability, and transparency

Module 4: Responsible AI: Applying AI Principles with Google Cloud

Estimated time: 1 hour

  • Operationalize responsible AI principles
  • Use Google Cloud tools to monitor AI systems
  • Apply AI governance frameworks in practice

Module 5: Final Project

Estimated time: 0.5 hours

  • Design a generative AI use case
  • Apply responsible AI principles to your scenario
  • Present recommendations using Google Cloud insights

Prerequisites

  • No prior AI or programming experience required
  • Basic familiarity with cloud computing concepts
  • Access to a web browser and Coursera platform

What You'll Be Able to Do After

  • Explain the fundamentals of generative AI and its applications
  • Describe how large language models work and how to tune prompts
  • Apply responsible AI practices in real-world contexts
  • Use Google Cloud tools to support ethical AI deployment
  • Integrate generative AI knowledge into professional workflows
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