Successful AI projects start with clarity, not code. This short, hands-on course helps you turn vague business goals into structured, measurable, and feasible AI problem statements. You’ll learn to evaluate whether your data is ready for modeling, estimate labeling requirements, and identify early risks such as imbalance, poor quality, or limited resources. Using real-world scenarios, you’ll apply the SMART framework to define objectives that are specific, measurable, achievable, relevant, and time-bound. By connecting business outcomes with technical success metrics like precision and recall, you’ll gain the confidence to frame AI projects that deliver measurable impact and align teams from idea to implementation.
Frame AI Problems: Objectives to Metrics
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Frame AI Problems: Objectives to Metrics
This course is part of Managing AI Projects That Ship and Scale Specialization
Instructor: ansrsource instructors
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
- Data Preprocessing
- AI Enablement
- Goal Setting
- Project Estimation
- Business Risk Management
- Requirements Analysis
- Due Diligence
- Key Performance Indicators (KPIs)
- Smart Goals
- Business Metrics
- Project Design
- Needs Assessment
- Business Requirements
- Business Priorities
- Risk Analysis
- Model Evaluation
- Project Scoping
- Project Risk Management
- Feasibility Studies
- Performance Metric
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
December 2025
Taught in English
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This course is part of the Managing AI Projects That Ship and Scale Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in this course
Successful AI projects start with clarity, not code. This short, hands-on course helps you turn vague business goals into structured, measurable, and feasible AI problem statements. You’ll learn to evaluate whether your data is ready for modeling, estimate labeling requirements, and identify early risks such as imbalance, poor quality, or limited resources. Using real-world scenarios, you’ll apply the SMART framework to define objectives that are specific, measurable, achievable, relevant, and time-bound. By connecting business outcomes with technical success metrics like precision and recall, you’ll gain the confidence to frame AI projects that deliver measurable impact and align teams from idea to implementation.
What's included
4 videos5 readings6 assignments
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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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