Managing Machine Learning Projects with Google Cloud
A self-paced course designed and organised by Google Cloud training that will permit you to learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. If you want to learn how to utilise machine learning in an easy way, without all the technical jargon and have questions about it, this course is for you. The course is aimed especially at non-technical business professionals who wants to lead or influence machine learning projects in their organisations and enterprises.
What will you learn in this course?
In this course you will learn how to identify the phases of an ML project and the factors that go into each one, as well as how to find unexpected use cases. Students will rapidly gain the confidence to present a unique ML use case to their team or leadership or translate the needs to a technical team.
In particular, participants enrolling in this course will learn how to:
- Explore common machine learning use cases implemented by businesses.
- Assess the feasibility of your own ML use case and its ability to meaningfully impact your business.
- Identify the requirements to build, train, and evaluate an ML model.
- Define data characteristics and biases that affect the quality of ML models and recognize key considerations for managing ML projects.
The course is self-paced with flexible deadlines according to your schedule, and it takes approximately 11 hours to be completed.
The course is part of the Digital Transformation Using AI/ML with Google Cloud Specialization, also offered by Google Cloud Training.
