Applied Machine Learning for Business Solutions opens the AI Engineering specialization by teaching machine learning specifically through a software engineer's lens — solving business problems, not just building models.
What D789 covers
The course familiarizes students with machine learning through the lens of a software engineer, exploring the challenges and opportunities of applying ML to solve problems and create strategic objectives.
Students explore various industries and learn to apply ML to address business needs, demonstrating how to effectively communicate recommendations to a range of stakeholders.
The D789 performance assessment
Expect a performance assessment requiring you to identify a business problem an ML solution could address and communicate a recommendation to a specific stakeholder audience.
Key topics in D789
- ML from a software engineering perspective
- Applying ML to business needs across industries
- Communicating ML recommendations to stakeholders
Writing tips for D789
Follow the task instructions and rubric line by line
WGU performance assessments for D789 are graded against a fixed rubric — every rubric line has to be visibly addressed, usually with a labeled heading that mirrors the rubric language. Skipping a rubric point because it seems minor is the single most common reason a competent submission comes back "Not Yet Competent" for revision.
Show your actual code and test results, not just a description of what you built
WGU evaluators are trained to distinguish genuine software engineering work from a paraphrased summary. Include your actual code, along with evidence it was tested (test cases, output, screenshots) — a rubric checking technical competency wants to see the working artifact and proof it functions.
Because WGU is self-paced, don't let "no deadline pressure" become no submission
There's no weekly due date forcing progress, which means procrastination costs more at WGU than at a traditional term-based school — a stalled task can quietly eat weeks of a term. Treat your own target date for each D789 assessment as a real deadline.
Stuck on your D789 task?
Our writers know WGU's competency-based format and this course's performance assessment. Get an original, properly cited paper matched to your task instructions.
Why students seek help with D789
Students sometimes focus purely on the technical ML approach and underdevelop the stakeholder communication component the course specifically requires — a complete response addresses both the technical solution and its business communication.
How GradeEssays helps with D789
Share your business problem and rubric, and your writer will build both the ML solution approach and a stakeholder-appropriate communication of the recommendation.
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Share your task instructions and rubric and we match you with a writer who knows this course and WGU's evaluation standards.
Place Your Order View All ServicesPrerequisites and program context
D789 has no prerequisites and opens the AI Engineering specialization.