Course overview
The AI course helps learners understand what modern AI can and cannot do, then apply it to research, content, analysis, productivity and business workflow problems with human review and responsible handling.
Learning outcomes
- Explain core AI and machine-learning concepts
- Write structured prompts and evaluate outputs
- Identify suitable automation opportunities
- Handle accuracy, privacy and bias risks
- Build small guided workflow prototypes
- Document human-review controls
Curriculum
01 · AI foundationsModels, training, inference and common AI system categories.
02 · Generative AIPrompt structure, context, output evaluation and iteration.
03 · Business use casesResearch, documents, analysis, customer service and operations.
04 · Workflow automationTriggers, data movement, approvals and human review.
05 · Responsible useAccuracy, privacy, intellectual property and bias awareness.
06 · Practical projectDefine, prototype, test and present a focused AI workflow.
Practical assignment
Example task
Select one repetitive business workflow, define approved inputs and outputs, prototype an AI-assisted step, test accuracy and failure cases, and document the required human review.
Who this course suits
- Students and career starters
- Business and operations professionals
- Marketing and content teams
- Managers evaluating automation opportunities
Learning format and prerequisites
Guided prompt work, output evaluation, automation mapping and a small practical project with review checkpoints.
No programming background is required for the business track. Technical projects may require basic Python.
Before enrolling: confirm the syllabus, trainer, schedule, delivery mode, practical access, fee and support included in the current batch.