Course Outline

 

Domain I AI Fundamentals

  • Understanding of AI Fundamentals and Evolution
  • Evaluating AI Applications and Patterns
  • Applying Machine Learning Fundamentals

Domain II CPMAI Methodology

  • Differentiating AI Project Management Approaches
  • Executing the Business Understanding Phase
  • Managing the Data Understanding Phase
  • Coordinating the Data Preparation Activities
  • Determining the Approaches for Model Development
  • Conducting Model Evaluation and Maintenance

Domain III Machine Learning

  • Applying Classification and Clustering Algorithms
  • Implementing Neural Networks and Deep Learning
  • Leveraging Generative AI and Large Language Models (LLMs)
  • Selecting Machine Learning Tools and Platforms

Domain IV Data for AI

  • Managing Data Fundamentals and Big Data Concepts
  • Implementing Data Governance and Management
  • Engineering Data Pipelines for AI
  • Executing Data Preparation and Transformation

Domain V Managing AI

  • Evaluating Model Performance and Accuracy
  • Deploying Models for Production Environments

Domain VI Trustworthy AI

  • Establishing Ethical, Responsible, and Trustworthy AI Foundations
  • Implementing AI Privacy and Security
  • Ensuring AI Transparency and Explainability
  • Navigating AI Regulations and Frameworks

Summary and Next Steps

Requirements

  • CPMAI v7 does not require any prior work experience, technical knowledge or AI experience, certifications, or
    prerequisites to enroll in the course and take the exam.

Audience

  • Project, program, and product managers
  • Data professionals
  • Technologists, Consultants, and IT professionals
  • Directors and Senior Managers leading AI initiatives
 21 Hours

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