Udemy - Frame ML Projects - Turn Business Needs into Real Solutio...
- Category Other
- Type Tutorials
- Language English
- Total size 1,022.6 MB
- Uploaded By freecoursewb
- Downloads 103
- Last checked 1 week ago
- Date uploaded 5 months ago
- Seeders 5
- Leechers 0
Infohash : 591B078679B0F125F47829F5844C4CEB6D769200
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.
Files:
[ WebToolTip.com ] Udemy - Frame ML Projects - Turn Business Needs into Real Solutions- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Introduction
- 1 -Why Most ML Projects Fail — and How Framing Fixes It.mp4 (34.0 MB)
- 2 -How We Use AI to Deliver This Course.mp4 (21.9 MB)
- 3 -Who This Course Is For.mp4 (7.7 MB)
- 4 -What You’ll Walk Away With.mp4 (14.7 MB)
- 5 -The Role of a Problem Framer.mp4 (10.2 MB)
- 6 -Where Framing Fits in the ML Lifecycle.mp4 (9.4 MB)
- 1 -Final Recap & Framing Checklist.mp4 (28.0 MB)
- 2 -Applying Framing in Your Role & Resume.mp4 (20.8 MB)
- 1 -Why ML Projects Fail.mp4 (29.0 MB)
- 2 -Cost of Poorly Scoped Problems.mp4 (29.3 MB)
- 3 -The Framing Framework Overview.mp4 (37.4 MB)
- 1 -Business Questions vs ML Problems.mp4 (31.9 MB)
- 2 -Components of a Well-Defined Problem.mp4 (29.3 MB)
- 3 -Common Pitfalls & Anti-Patterns.mp4 (27.5 MB)
- 1 -Are We Predicting or Just Describing.mp4 (24.9 MB)
- 2 -Are the Signals Strong Enough.mp4 (25.1 MB)
- 3 -Do We Have Outcome Labels.mp4 (25.2 MB)
- 1 -Defining Business Success Metrics.mp4 (33.3 MB)
- 2 -Translating Metrics into ML Terms.mp4 (26.6 MB)
- 3 -Aligning ML KPIs with Business Goals.mp4 (24.6 MB)
- 4 -Success Criteria Checklist.mp4 (30.0 MB)
- 1 -Mapping Stakeholders.mp4 (30.2 MB)
- 2 -Understanding Stakeholder Pain Points.mp4 (33.5 MB)
- 3 -Asking the Right Questions.mp4 (29.0 MB)
- 4 -Stakeholder Alignment Techniques.mp4 (31.3 MB)
- 5 -Communicating Framing with Artifacts.mp4 (23.0 MB)
- 1 -Technical Feasibility.mp4 (38.1 MB)
- 2 -Data Availability & Quality.mp4 (44.2 MB)
- 3 -Resource & Timeline Constraints.mp4 (28.7 MB)
- 4 -Ethical & Legal Considerations.mp4 (34.4 MB)
- 1 -Identifying Risks Early.mp4 (34.1 MB)
- 2 -Listing and Validating Assumptions.mp4 (32.5 MB)
- 3 -Planning for Feedback Loops.mp4 (31.5 MB)
- 1 -Case From Vague Request to Framed Problem.mp4 (35.2 MB)
- 2 -Case Scoping & Metrics in Action.mp4 (37.4 MB)
- 3 -Case Feasibility, Risks & Summary.mp4 (38.7 MB)
- Bonus Resources.txt (0.1 KB)
There are currently no comments. Feel free to leave one :)
Code:
- udp://tracker.torrent.eu.org:451/announce
- udp://tracker.tiny-vps.com:6969/announce
- http://tracker.foreverpirates.co:80/announce
- udp://tracker.cyberia.is:6969/announce
- udp://exodus.desync.com:6969/announce
- udp://explodie.org:6969/announce
- udp://tracker.opentrackr.org:1337/announce
- udp://9.rarbg.to:2780/announce
- udp://tracker.internetwarriors.net:1337/announce
- udp://ipv4.tracker.harry.lu:80/announce
- udp://open.stealth.si:80/announce
- udp://9.rarbg.to:2900/announce
- udp://9.rarbg.me:2720/announce
- udp://opentor.org:2710/announce