| 1 -Are We Predicting or Just Describing.mp4 | 24.9 MB | ||
| 1 -Business Questions vs ML Problems.mp4 | 31.9 MB | ||
| 1 -Case From Vague Request to Framed Problem.mp4 | 35.2 MB | ||
| 1 -Defining Business Success Metrics.mp4 | 33.3 MB | ||
| 1 -Final Recap & Framing Checklist.mp4 | 28 MB | ||
| 1 -Identifying Risks Early.mp4 | 34.1 MB | ||
| 1 -Mapping Stakeholders.mp4 | 30.2 MB | ||
| 1 -Technical Feasibility.mp4 | 38.1 MB | ||
| 1 -Why ML Projects Fail.mp4 | 29 MB | ||
| 1 -Why Most ML Projects Fail — and How Framing Fixes It.mp4 | 34 MB | ||
| 2 -Applying Framing in Your Role & Resume.mp4 | 20.8 MB | ||
| 2 -Are the Signals Strong Enough.mp4 | 25.1 MB | ||
| 2 -Case Scoping & Metrics in Action.mp4 | 37.4 MB | ||
| 2 -Components of a Well-Defined Problem.mp4 | 29.3 MB | ||
| 2 -Cost of Poorly Scoped Problems.mp4 | 29.3 MB | ||
| 2 -Data Availability & Quality.mp4 | 44.2 MB | ||
| 2 -How We Use AI to Deliver This Course.mp4 | 21.9 MB | ||
| 2 -Listing and Validating Assumptions.mp4 | 32.5 MB | ||
| 2 -Translating Metrics into ML Terms.mp4 | 26.6 MB | ||
| 2 -Understanding Stakeholder Pain Points.mp4 | 33.5 MB | ||
| 3 -Aligning ML KPIs with Business Goals.mp4 | 24.6 MB | ||
| 3 -Asking the Right Questions.mp4 | 29 MB | ||
| 3 -Case Feasibility, Risks & Summary.mp4 | 38.7 MB | ||
| 3 -Common Pitfalls & Anti-Patterns.mp4 | 27.5 MB | ||
| 3 -Do We Have Outcome Labels.mp4 | 25.2 MB | ||
| 3 -Planning for Feedback Loops.mp4 | 31.5 MB | ||
| 3 -Resource & Timeline Constraints.mp4 | 28.7 MB | ||
| 3 -The Framing Framework Overview.mp4 | 37.4 MB | ||
| 3 -Who This Course Is For.mp4 | 7.7 MB | ||
| 4 -Ethical & Legal Considerations.mp4 | 34.4 MB | ||
| 4 -Stakeholder Alignment Techniques.mp4 | 31.3 MB | ||
| 4 -Success Criteria Checklist.mp4 | 30 MB | ||
| 4 -What You’ll Walk Away With.mp4 | 14.7 MB | ||
| 5 -Communicating Framing with Artifacts.mp4 | 23 MB | ||
| 5 -The Role of a Problem Framer.mp4 | 10.2 MB | ||
| 6 -Where Framing Fits in the ML Lifecycle.mp4 | 9.4 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 38 total files | |||
Frame ML Projects: Turn Business Needs into Real Solutions
https://WebToolTip.com
Published 7/2025
Created by Hemanth Kumar K
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 36 Lectures ( 3h 6m ) | Size: 1 GB
Learn how to frame machine learning projects the right way—used by real data science and product teams to reduce rework
What you'll learn
Distinguish vague business asks from real ML problems, and translate them into tasks like classification, ranking, or regression.
Define success in business terms, then align model KPIs like precision, recall, or F1 with actual usage, trust, and lifecycle goals.
Surface hidden risks, test assumptions early, and assess feasibility across data quality, infra readiness, and ethical constraints.
Use one-pagers, stakeholder maps, and alignment templates to frame ML projects clearly and earn buy-in without technical overload.
Requirements
No coding or ML experience required. Basic familiarity with business goals, analytics, or project work is helpful but not mandatory.
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