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

سرفصل های دوره

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.

01 - Introduction

01 - Course and Google Professional Machine Learning Engineer exam overview

02 - Course 1 key terminology

02 - 1. Translating Business Challenges into ML Use Cases

01 - Building AI-enabled workflows

02 - Using AI tools to build AI tools

03 - Teaching MLOps at scale with GitHub

03 - 2. Defining ML Problems

01 - Simulations vs. experiment tracking

02 - When to use ML

03 - Supervised vs. unsupervised ML

04 - Optimization

05 - Clustering

04 - 3. Defining Business Success Criteria

01 - Defining business success criteria

05 - 4. Identifying Risks to Feasibility of ML Solutions

01 - MLOps hierachy of needs

02 - Hidden costs of bespoke systems

03 - Data poisoning

06 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)