Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
Managing Machine Learning Projects with Google Cloud
This course is part of Digital Transformation Using AI/ML with Google Cloud Specialization
Instructor: Google Cloud Training
Sponsored by Google People Development
122,187 already enrolled
(3,661 reviews)
What you'll learn
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.
Skills you'll gain
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There are 8 modules in this course
Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.
What's included
1 video2 readings
This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.
What's included
4 videos1 reading1 assignment
This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.
What's included
9 videos1 reading1 assignment
After you have assessed the feasibility of your supervised ML problem, you're ready to move to the next phase of an ML project. This module explores the various considerations and requirements for building a complete dataset in preparation for training, evaluating, and deploying an ML model. It also includes two demos—Vision API and AutoML Vision—as relevant tools that you can easily access yourself or in partnership with a data scientist. You'll also have the opportunity to try out AutoML Vision with the first hands-on lab.
What's included
6 videos1 assignment1 app item
Data in the world is inherently biased, and that bias can be amplified through ML solutions. In this module, you'll learn about some of the most common biases and how they can disproportionately affect or harm an individual or groups of individuals. You'll also be given guidelines for uncovering possible biases at each phase of an ML project and strategies for achieving ML fairness as much as possible.
What's included
6 videos1 assignment1 app item
This module explores 5 general themes for discovering ML use cases within day-to-day business, followed by concrete customer examples. You'll learn about creative applications of ML, such as improving the resolution of images or generating music.
What's included
6 videos1 reading1 assignment1 app item
When you thoroughly understand the fundamentals of machine learning and considerations within in each phase of the project, you're ready to learn about the best practices for managing an ML project. This module describes 5 key considerations for successfully managing an ML project end-to-end: identifying the business value, developing a data strategy, establishing data governance, building successful ML teams, and enabling a culture of innovation. You'll also have an opportunity to gain further exposure to one of Google Cloud's tools by completing a final hands-on lab: Evaluate an ML Model with BigQuery ML.
What's included
7 videos1 assignment1 app item
This module provides a summary of the key points covered in each of the modules in the course.
What's included
1 video
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Reviewed on Jun 18, 2020
A precise combination of theory and lab oriented study which throws light on why, how ML can be beneficial encompassing ethical questions and a change in the culture needed to enforce ML.
Reviewed on May 4, 2020
This course is for a beginner who would like to learn how machine learning could do with business. Companies will benefit from this course to survive the digital transformed world.
Reviewed on Apr 29, 2020
Apt level of coverage for business professionals and project managers. There were few advertisements for the google training which can be minimised by just referring and not going too much in detail.
Recommended if you're interested in Data Science
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