Generative AI Course
Use generative AI responsibly for research, writing, analysis, presentations and repeatable workplace workflows.Course structure: 12 weeks of guided teaching, supervised practice,...
Understand the subject. Build practical ability.
Use generative AI responsibly for research, writing, analysis, presentations and repeatable workplace workflows.
Course structure: 12 weeks of guided teaching, supervised practice, assessment and portfolio evidence.
What you will learn
- AI literacy, limitations and safe use
- Clear prompting and output criteria
- Research summaries and source verification
- Business writing and customer communication
- Document summaries and structured information
- AI-assisted spreadsheets and calculation checks
- Presentations and business explanations
- Content repurposing and multimodal responsibility
- Reference-grounded business assistant
- Repeatable workflows and fair evaluation
- Capstone workflow testing
- Demonstration and independent judgement
Practical projects
- P01 - AI use-case and safety assessment
- P02 - Prompt comparison and improvement log
- P03 - Verified research-summary pack
- P04 - Customer communication draft library
- P05 - Policy summary and meeting-action register
- P06 - AI-assisted spreadsheet with audit checks
- P07 - Source-grounded business presentation
- P08 - Multi-format content repurposing pack
- P09 - Bounded FAQ assistant evaluation
- P10 - Reviewed AI business-workflow capstone
Learning standard: Learners retain working files, perform quality checks, correct mistakes, disclose permitted AI assistance and demonstrate the final work independently.
What you will learn
- Overview of generative AI concepts and real‑world use cases.
- Core machine‑learning principles needed for generative modeling.
- Deep‑learning building blocks such as neural networks and backpropagation.
- Fundamentals of Generative Adversarial Networks (GANs) and training dynamics.
- Introduction to Variational Autoencoders and diffusion models.
- Practical applications of generative AI in media, design, and business.
- Ethical considerations, bias mitigation, and responsible deployment.
- Strategies for deploying, scaling, and monitoring generative models in production.
- Emerging research directions and future trends in generative AI.
Course curriculum
Module 1Overview of generative AI concepts and real‑world use cases.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 2Core machine‑learning principles needed for generative modeling.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 3Deep‑learning building blocks such as neural networks and backpropagation.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 4Fundamentals of Generative Adversarial Networks (GANs) and training dynamics.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 5Introduction to Variational Autoencoders and diffusion models.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 6Practical applications of generative AI in media, design, and business.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 7Ethical considerations, bias mitigation, and responsible deployment.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 8Strategies for deploying, scaling, and monitoring generative models in production.
Instructor guidance, practical exercises and applied assignments are included in this module.
Module 9Emerging research directions and future trends in generative AI.
Instructor guidance, practical exercises and applied assignments are included in this module.