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Google Cloud Professional Machine Learning Engineer Cert Prep: 1 Framing ML Problems

Offered By: LinkedIn Learning

Tags

Machine Learning Courses Supervised Learning Courses Unsupervised Learning Courses MLOps Courses Clustering Courses Data Poisoning Courses

Course Description

Overview

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Learn about the Google Professional Machine Learning Engineer certification exam, and topics relating to the first part of the exam, framing ML problems.

Syllabus

Introduction
  • Course and Google Professional Machine Learning Engineer exam overview
  • Course 1 key terminology
1. Translating Business Challenges into ML Use Cases
  • Building AI-enabled workflows
  • Using AI tools to build AI tools
  • Teaching MLOps at scale with GitHub
2. Defining ML Problems
  • Simulations vs. experiment tracking
  • When to use ML
  • Supervised vs. unsupervised ML
  • Optimization
  • Clustering
3. Defining Business Success Criteria
  • Defining business success criteria
4. Identifying Risks to Feasibility of ML Solutions
  • MLOps hierachy of needs
  • Hidden costs of bespoke systems
  • Data poisoning
Conclusion
  • Next steps

Taught by

Noah Gift

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