Fundamentals of Modern AI Course
IDTECH7000

Module 1: Key AI Concepts

• Differentiate machine learning, deep learning, and foundational/generative models.
• Understand how neural networks underpin modern AI systems.
• Situate today’s GenAI within the broader evolution of AI.

Module 2: Chatbots & Prompt Engineering

• Explore how chatbots work and where they’re useful.
• Practice core techniques like Chain-of-Thought and RAG to improve responses.
• Apply prompt patterns to control tone, format, and accuracy.

Module 3: Copilots & Assistants

• Survey productivity copilots (e.g., Microsoft 365 Copilot, Acrobat AI Assistant).
• Automate writing, summarization, and document workflows.
• Identify governance and access considerations for enterprise rollout.

Module 4: Languages & Platforms for AI

• Review common languages and platforms used to build and run AI solutions.
• Compare strengths of hosted tools vs. developer-centric stacks.
• Recognize when to prototype vs. productize.

Module 5: Cloud Computing & AI

• Connect cloud services to AI hosting, scaling, and MLOps.
• Map data pipelines and vector stores to model use.
• Identify managed AI services vs. custom deployments.

Module 6: Planning for AI

• Outline an AI roadmap aligned to business goals.
• Define roles, resourcing, and change management.
• Establish criteria for “AI-ready” people, process, and data.

Module 7: Risks of AI

• Identify key risks: accuracy, bias, privacy, IP, and security.
• Design guardrails and review checkpoints.
• Balance speed with risk tolerance.

Module 8: Responsible/Ethical AI

• Apply ethical principles to model selection and deployment.
• Operationalize transparency, accountability, and human oversight.
• Measure and monitor for unintended harm.

Module 9: Image Generation & Diffusion Models

• Understand how diffusion models generate images.
• Control style, composition, and iteration through prompting.
• Discuss licensing and content provenance.

Module 10: (More) AI Tools

• Compare emerging tools across writing, data, and media tasks.
• Match tools to use cases and team skill levels.
• Create a shortlist for pilot testing.

Module 11: Cybersecurity

• Survey ML in cybersecurity and enterprise security platforms.
• Review cloud security options (e.g., AWS security services).
• Plan for secure AI adoption and monitoring.