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.

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.

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.

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.

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.

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.

This module provides a summary of the key points covered in each of the modules in the course.