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 specialization
-Introduces the specialization and the Google experts who will be teaching it.
What it means to be AI first
-You will learn what we mean when we say that Google’s company strategy is to be AI-first, and what that means in practice.
How Google does ML
-This module is about the organizational know-how Google has acquired over the years.
-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
-This module covers Cloud Datalab, which is the development environment you will use in this specialization.
-Review the core ML topics that this specialization will cover.
Google Cloud Training
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Aseem completed this course, spending 8 hours a week on it and found the course difficulty to be easy.
I already knew bit about google cloud so the parts related to that were not new for me. But the parts where they were talking ML strategy was totally new for me. Some of it like replacing rules by ML totally blew my mind. First time hearing that stuff.