What you will learn in Introduction to Generative AI Learning Path Specialization Course
Understand the fundamentals of generative AI, including its definition, working principles, and applications.
Explore large language models (LLMs), their use cases, and prompt tuning techniques.
Gain insights into responsible AI practices and ethical considerations in AI development.
Learn how to apply AI principles using Google Cloud tools.
Program Overview
Course 1: Introduction to Generative AI
⏳ 1 hour
- Learn the basics of generative AI, how it works, and its various applications.
Course 2: Introduction to Large Language Models
⏳ 54 minutes
- Delve into the world of LLMs, their use cases, and techniques like prompt tuning.
Course 3: Introduction to Responsible AI
⏳ 17 minutes
- Understand the importance of responsible AI practices and how to implement them.
Course 4: Responsible AI: Applying AI Principles with Google Cloud
⏳ 1 hour
- Learn to operationalize responsible AI principles using Google Cloud’s tools and frameworks.
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Job Outlook
Completing this specialization prepares you for roles that involve AI integration and ethical AI practices.
The skills acquired are applicable across various industries that utilize AI technologies.
Enhance your employability by gaining practical experience in generative AI and responsible AI implementation.
Specification: Introduction to Generative AI Learning Path Specialization Course
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FAQs
- No prior AI experience is necessary.
- Basic familiarity with computers and the internet is sufficient.
- Course uses accessible explanations for non-programmers.
- Coding exercises are optional in early modules.
- Designed for learners from any background.
- Introduces AI tools for content creation, automation, and data analysis.
- Explains applications in marketing, design, and software development.
- Helps improve efficiency with AI-assisted workflows.
- Supports decision-making using AI-generated insights.
- Encourages exploration of AI opportunities in various industries.
- Focuses on understanding generative AI concepts and applications.
- Basic model-building is introduced but not at expert level.
- Emphasizes practical use cases rather than deep math.
- Prepares learners for further specialized AI courses.
- Suitable as a stepping stone toward AI engineering roles.
- Concepts are explained using real-world examples.
- Demonstrates AI tools for non-programming tasks.
- Helps managers and marketers leverage AI in workflows.
- Improves understanding of AI’s capabilities and limitations.
- Encourages adoption of AI without deep technical knowledge.
- Focuses specifically on generative AI techniques like text, image, and code generation.
- Explains AI-driven creativity and content synthesis.
- Less emphasis on traditional ML algorithms and statistics.
- Uses hands-on projects to demonstrate generative applications.
- Prepares learners for emerging AI trends rather than foundational ML theory.
