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IT Academy: Introduction to AI

This introductory course provides a comprehensive foundation in Artificial Intelligence (AI), empowering students to work confidently with AI tools, systems, and methodologies across diverse professional settings. They will explore the scope and key disciplines of AI, including data strategies, model architectures, and the complete AI lifecycle. Through activities, students will apply prompt engineering techniques to enhance content generation, data analysis, and problem-solving. They will also integrate responsible AI principles into their work and be equipped to analyze the societal and workforce impacts of AI technologies across different contexts. By engaging with real-world scenarios and applied projects, students will learn to design, assess, and adapt AI solutions that are effective, fair, and sustainable.

  1. Define, differentiate, and contextualize foundational AI concepts, paradigms, and models.

  2. Evaluate AI-ready data strategies by assessing structured and unstructured datasets, applying data quality enhancement techniques, and implementing governance principles.

  3. Explain and illustrate the complete lifecycle of AI systems from data acquisition and preparation through training, evaluation, fine-tuning, deployment, and inference.

  4. Develop and implement effective prompt engineering strategies to optimize interactions with generative AI tools for tasks such as content generation, data analysis, and problem-solving.

  5. Select, adapt, and justify the use of AI models, architectures, and tools based on application requirements, interpretability needs, scalability, infrastructure constraints, and integration considerations.

  6. Assess the performance, limitations, and risks of AI systems, and implement mitigation strategies for threats using guardrails, retrieval-augmented generation, and defensive architectures.

  7. Integrate responsible AI principles into all stages of AI development and ensure compliance with relevant legal, regulatory, and governance frameworks.

  8. Analyze societal, workforce, and environmental impacts of AI, and synthesize and forecast emerging trends to anticipate opportunities and challenges.

Course Content

Topical areas of study include:

  • Foundations of Artificial Intelligence

  • Data Strategy for AI

  • AI Model Architectures and Ecosystem

  • AI Model Lifecycle and Deployment

  • Prompt Engineering

  • AI Reliability, Security, and Risk Mitigation

  • AI Ethics, Governance, and Responsible AI

  • Applications, Infrastructure, and Societal Impact

Employee registration is coordinated by the Program Manager supporting your training. Depending on the course, employees may register in person, through an organization-provided roster, or via a self-registration link. Your Program Manager will guide you through the best option for your specific training.

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