Google Cloud
Managing Machine Learning Projects with Google Cloud
Google Cloud

Managing Machine Learning Projects with Google Cloud

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Course

Gain insight into a topic and learn the fundamentals

4.6

(3,615 reviews)

|

93%

Beginner level
No prior experience required
13 hours (approximately)
Flexible schedule
Learn at your own pace

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.

Details to know

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Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(3,615 reviews)

|

93%

Beginner level
No prior experience required
13 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Digital Transformation Using AI/ML with Google Cloud Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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 quiz

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 quiz

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 quiz1 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 quiz1 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 quiz1 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 quiz1 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

Instructor

Instructor ratings
4.6 (1,018 ratings)
Google Cloud Training
Google Cloud
1,396 Courses2,564,175 learners

Offered by

Google Cloud

Recommended if you're interested in Machine Learning

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4.6

3,615 reviews

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