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

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Noah Gift

1:07:23

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01 - Introduction

01 - Course and Google Professional Machine Learning Engineer exam overview.mp4

03:34

02 - Course 1 key terminology.mp4

04:00

02 - 1. Translating Business Challenges into ML Use Cases

01 - Building AI-enabled workflows.mp4

01:39

02 - Using AI tools to build AI tools.mp4

01:52

03 - Teaching MLOps at scale with GitHub.mp4

28:58

03 - 2. Defining ML Problems

01 - Simulations vs. experiment tracking.mp4

06:12

02 - When to use ML.mp4

01:53

03 - Supervised vs. unsupervised ML.mp4

02:23

04 - Optimization.mp4

02:16

05 - Clustering.mp4

02:08

04 - 3. Defining Business Success Criteria

01 - Defining business success criteria.mp4

02:39

05 - 4. Identifying Risks to Feasibility of ML Solutions

01 - MLOps hierachy of needs.mp4

03:05

02 - Hidden costs of bespoke systems.mp4

02:24

03 - Data poisoning.mp4

02:50

06 - Conclusion

01 - Next steps.mp4

01:30

Description

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.

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Machine Learning

Google Cloud Certification

Cloud Computing

Google Cloud

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.