How Google does Machine Learning

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.

Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

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• Introduction to Course
• Introduces the specialization and the Google experts who will be teaching it.
• What it means to be AI first
• In this module, you explore building a data strategy around machine learning.
• How Google does ML
• This module is about the organizational know-how Google has acquired over the years.
• Inclusive ML
• This module will discuss why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.
• Python Notebooks in the cloud
• Understand the role of AI Platform Notebooks
• Summary
• Review the core ML topics that this specialization will cover.