Designing and Implementing Solutions Using Google Machine Learning APIs

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.
Course info
Rating
(15)
Level
Beginner
Updated
Oct 19, 2018
Duration
1h 38m
Table of contents
Description
Course info
Rating
(15)
Level
Beginner
Updated
Oct 19, 2018
Duration
1h 38m
Description

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.

About the author
About the author

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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi, my name is Janani Ravi, and welcome to this course on Designing and Implementing Solutions Using Google Machine Learning APIs. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. In this course, we'll see how you can use and work with Google machine learning APIs, which make powerful pre-trained models trained on Google's vast datasets available to anyone with basic programming ability. We start off with an overview of the suite of machine learning services available on the Google Cloud, and understand the features of each, so we can make the right choice about what service makes sense for our use case. We'll then work with speech-based APIs, which allow us to convert speech to text and text to speech, with additional emphasis support using SSML. We'll see how we can call these REST APIs using simple Python libraries. We'll understand the configuration options available with each API, and also learn their limitations. We'll then study the Natural Language APIs and see how they can be used for sentiment analysis and for language translation. Cloud ML APIs supports 700+ pre-defined categories for content classification, and has support for syntax analysis as well as entity analysis. Finally, we'll work with the Vision and Video Intelligence APIs, which allow us to perform face and label detection on images. The video APIs employ cutting-edge models to enable you to detect scene changes, explicit content, and perform safe search. At the end of this course, you'll be very comfortable choosing the right ML API that fits your use case, and using multiple APIs together to build more complex features for your product.