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Google Cloud Professional Machine Learning Engineer Cert Prep: 1 Framing ML Problems
Via LinkedIn Learning
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Overview
“Google Cloud Professional Machine Learning Engineer Cert Prep: 1 Framing ML Problems” is an essential course module that helps learners understand how to properly define and scope machine learning problems. Correctly framing ML problems is crucial for selecting the right algorithms, data, and evaluation methods to build effective models. Participants learn to translate business challenges into ML tasks by identifying input features, target variables, and success criteria. The course covers problem types such as classification, regression, clustering, and recommendation systems,...
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
Instructor
Noah Gift
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