English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 37m | 252 MB
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.
Table of Contents
Course Overview
1 Course Overview
Introducing the Google Cloud ML APIs
2 Module Overview
3 Prerequisites and Course Outline
4 Introducing Google Cloud AI
5 Cloud ML APIs vs. ML Engine and AutoML
6 Cloud Storage and Cloud Shell
7 Enabling APIs and Setting up Service Account Keys
Working with Speech and Text Using the Cloud ML APIs
8 Module Overview
9 Introducing the Speech-to-Text API
10 Speech-to-Text Using gcloud
11 Speech-to-Text Using the REST API
12 Connecting to Datalab
13 Synchronous and Asynchronous Speech-to-Text
14 Speech-to-Text with Raw Audio
15 Introducing the Text-to-Speech API
16 Installing Text-to-Speech Libraries
17 Speech Synthesis in Multiple Languages Using Text-to-Speech
18 Text-to-Speech Using SSML
Working with Language Using the Cloud ML APIs
19 Module Overview
20 Introducing the Natural Language API
21 Content Classification Using the Natural Language API
22 Entity Analysis Using the Natural Language API
23 Content Classification on a Text File Stored in Cloud Storage
24 Sentiment Analysis Using the Natural Language API
25 Introducing the Translate API
26 Text Translation Using the Translate API
Working with Images and Videos Using the Cloud ML APIs
27 Module Overview
28 Introducing the Cloud Vision API
29 Label Detection
30 Face Detection
31 Optical Character Recognition
32 Introducing the Video Intelligence APIs
33 Videos Analysis
34 Explicit Content Detection
35 Summary and Further Study
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