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Fundamentals of Generative AI

Microsoft Power Platform helps organizations optimize their operations by simplifying, automating and transforming business tasks and processes. In this course, students will discuss the various methods and best practices that are aligned with the commercial and technical requirements for modeling, visualizing and analyzing data with Power BI. This training will also show you how to access and process data from a variety of data sources, including relational and non-relational data.

In addition, it will also teach how to implement proper security standards and policies across the Power BI spectrum, including data sets and groups. The course will also discuss how to manage and deploy reports and dashboards to share and distribute content.

  • Duration
    70h (Online)

Objectives

This course teaches you the skills needed to successfully complete the Generative AI Fundamentals certification offered by Certiport. These skills are introduced through various types of exercises and review materials. 

  • Upon completion of this course, you will understand:
  • Understand the key concepts of Generative AI: Explore the essential methods of AI, including transformers, GANs, and broadcast models, and how they produce outputs such as text, images, video, and audio.
  • Master basic prompt engineering: Learn how to create and modify effective prompts to generate and transform content, adapting the results to specific tasks and media formats.
  • Refine prompt techniques: Improve prompt clarity and accuracy by using common strategies, such as zero-shot and few-shot prompting, for more accurate and focused AI responses.
  • Recognize ethical and social impacts: It comprises key ethical considerations, such as bias, data privacy, and legal implications, and explores the broader societal impacts of using AI in various fields.

Program

  • UNIT 1: Introduction.
  • Module 1: Types of AI.
  • Module objectives and key terms.
  • Artificial Intelligence.
  • Generative AI.
  • Predictive AI.
  • Discriminative AI.
  • Analytical AI.
  • Statistical AI.
  • AI vs. AI Search Engines.
  • Module 2: Processes.
  • Module objectives and key terms.
  • Text models.
  • Image models.
  • Large language models.
  • Dissemination.
  • Transformer.
  • Variational autoencoders.
  • Generative Antagonistic Networks.
  • Convolutional Neural Networks.
  • Model training.
  • Module 3: Entry and Exit.
  • Module objectives and key terms.
  • Understand the input and output.
  • Types of input.
  • Types of output.
  • Customization.
  • AI tools.
  • Selecting a tool.
  • Module 4: Tools and Systems.
  • Objectives of the Module and key terms.
  • Conversational models.
  • ChatGPT.
  • Microsoft Copilot.
  • Google Gemini.
  • Meta AI.
  • Adobe Express.
  • Claude.
  • Microsoft Azure AI Studio.
  • Stable Diffusion.
  • DALL-E.
  • Adobe Firefly.
  • Module 4: Tools and Systems.
  • Objectives of the Module and key terms.
  • Conversational models.
  • ChatGPT.
  • Microsoft Copilot.
  • Google Gemini.
  • Meta AI.
  • Adobe Express.
  • Claude.
  • Microsoft Azure AI Studio.
  • Stable Diffusion.
  • DALL-E.
  • Adobe Firefly.
  • Module 5: Limitations.
  • Objectives of the Module and key terms.
  • Reliability.
  • Technology requirements.
  • Privacy.
  • Lack of standards.
  • Consistency.
  • Obsolescence.
  • UNIT 2: Prompts engineering.
  • Module 1: Textual information.
  • Module objectives and key terms.
  • Collect content.
  • Summarize.
  • Create and ideate.
  • Module 2: Transforming content.
  • Module objectives and key terms.
  • Reformat content.
  • Edit and proofread.
  • Visualize.
  • Transform media type.
  • Personalize and adapt.
  • Module 3: Images.
  • Produce an image.
  • Explore artistic ideas.
  • Transform an image.
  • Describe an image.
  • Module 4: Video.
  • Add movement.
  • Interpolate.
  • Coloring.
  • Generate video.
  • Generate an avatar.
  • Add and remove objects.
  • Subtitles.
  • UNIT 3: Refinement of Prompts.
  • Module 1: Improvements.
  • Module objectives and key terms.
  • Specificity.
  • Clarity.
  • Avoid assumptions.
  • Style guide.
  • Person.
  • Context.
  • Module 2: Additional inputs.
  • Module objectives and key terms.
  • Examples.
  • Glossary.
  • Templates.
  • Research papers.
  • Previous conversations.
  • Module 3: Prompt techniques.
  • Module objectives and key terms.
  • Zero-Shot.
  • Few-Shot.
  • Chain-of-Thought.
  • Self-consistency.
  • Generate knowledge.
  • Prompt chaining.
  • Reverse prompting.
  • Module 4: Precision.
  • Module objectives and key terms.
  • Verify historical facts.
  • Verify current facts.
  • Verify numerical data.
  • UNIT 4: Ethics.
  • Module 1: Bias.
  • Module objectives and key terms.
  • Bias in AI.
  • Training data.
  • Guardrails.
  • Prompts.
  • Common biases.
  • Module 2: Legal implications.
  • Module objectives and key terms.
  • Intellectual property.
  • Inappropriate use.
  • Transparency.
  • Module 3: Privacy concerns.
  • Module objectives and key terms.
  • Importance of privacy.
  • Training.
  • Identity theft.
  • Company policies.
  • Human-generated content.
  • Module 4: Risks and impacts.
  • Module objectives and key terms.
  • Supervision.
  • Responsibility.
  • Legal action.
  • Dangerous purposes.
  • Negative impacts.
  • Positive impacts.
Admission
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