Most organizations wish to harness the power of machine learning (ML) to improve their products, but they may not always have the expertise available in-house. This course shows you how to harness the power of ML for use cases using API calls.
The Google Cloud Platform makes a wide range of machine learning (ML) services available as a part of Google Cloud AI. Google Cloud Machine Learning APIs are the most accessible and lightweight service which makes powerful ML models available to even novice programmers using simple, intuitive APIs. In this course, Designing and Implementing Solutions Using Google Machine Learning APIs, you'll learn how you can use and work with Google Machine Learning APIs, which makes powerful pre-trained models on Google’s datasets. First, you'll delve into an overview of the machine learning services suite available on the Google Cloud, and understand the features of each so you can make the right choice about what service makes sense for your use case. Next, you'll discover speech-based APIs allowing you to convert speech-to-text and text-to-speech with additional emphasis support using SSML, and how you can call these REST APIs using simple Python libraries. Then, you'll learn about Natural Language APIs and see how they can be used for sentiment analysis and for language translation. Finally, you'll explore the Vision and Video Intelligence APIs in order to perform face and label detection on images. By the end of this course, you'll have the necessary knowledge to choose the right ML API that fits your use case and use multiple APIs together to build more complex features for your product.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.