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Google Cloud Vision API by Example

Google Cloud Vision API by Example

1h 9mIntermediate2020-02-06

Authors

Jonathan Fernandes

Jonathan Fernandes

Consultant focusing on data science, AI, and big data

Course details

Google Cloud Vision API encapsulates powerful machine learning models in an easy-to-use REST API, allowing developers to leverage the power of machine learning without needing to train models of their own. Vision API gives you the power to annotate your images and text, detect objects and faces, automatically identify product logos and landmarks, and more. In this hands-on course, instructor Jonathan Fernandes helps you get up and running with this powerful product. Jonathan demonstrates how to make calls to the API with Python and leverage services that allow you to extract text from images, detect labels and facial expressions, and work effectively with batches of images.

Topics include:
- Detecting text with optical character recognition
- Navigating through the Vision API documentation
- How the Vision API can detect facial expressions
- Detecting and extracting multiple objects
- Preparing data for batch images
- Working with JSON and Pandas

Skills covered

Google CloudAPIsFull-Stack Web DevelopmentSoftware Development ToolsGoogleCloud PlatformsWeb DevelopmentCloud ComputingSoftware DevelopmentOne-Off

Concepts

0. Introduction

  • 01 - Machine learning for images made easier

1. Getting Started with Google Vision API

  • 02 - What is Google Vision API
  • 03 - Getting started
  • 04 - Testing setup

2. Optical Character Recognition and Label Detection

  • 05 - Detect text with optical character recognition (OCR)
  • 06 - Detect labels
  • 07 - Working with Google Cloud Vision API documentation

3. Face and Object Detection

  • 08 - Facial expression detection
  • 09 - Face detection
  • 10 - Detecting multiple objects
  • 11 - Challenge - Determine the landmark
  • 12 - Solution - Determine the landmark

4. Working with Batches

  • 13 - Data preparation for batch images
  • 14 - Creating the batch image request
  • 15 - Working with JavaScript Object Notation (JSON)
  • 16 - Working with Pandas

Conclusion

  • 17 - Clean up
  • 18 - Next steps

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