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

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

1h 7mAdvanced2023-06-08

Authors

Noah Gift

Noah Gift

MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO

Course details

Earning a Google Professional Machine Learning Engineer certification demonstrates your ability to design, build, and productionize machine learning models to solve business challenges using Google Cloud technologies, and knowledge of proven ML models and techniques.

This is the first course in the Google Professional Machine Learning Engineer certification prep series and covers framing machine learning problems. Instructor Noah Gift shows you how to translate business challenges into ML use cases, defines common ML problems and business success criteria, and identifies risks to feasibility of ML solutions.

Skills covered

Google CloudMachine LearningSoftware Development ToolsGoogleCloud PlatformsCert PrepArtificial Intelligence (AI)Cloud ComputingSoftware Development

Concepts

0. Introduction

  • 01 - Course and Google Professional Machine Learning Engineer exam overview
  • 02 - Course 1 key terminology

1. Translating Business Challenges into ML Use Cases

  • 03 - Building AI-enabled workflows
  • 04 - Using AI tools to build AI tools
  • 05 - Teaching MLOps at scale with GitHub

2. Defining ML Problems

  • 06 - Simulations vs. experiment tracking
  • 07 - When to use ML
  • 08 - Supervised vs. unsupervised ML
  • 09 - Optimization
  • 10 - Clustering

3. Defining Business Success Criteria

  • 11 - Defining business success criteria

4. Identifying Risks to Feasibility of ML Solutions

  • 12 - MLOps hierachy of needs
  • 13 - Hidden costs of bespoke systems
  • 14 - Data poisoning

Conclusion

  • 15 - Next steps

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