Hello and welcome to this Cloud Academy course on Amazon Rekgnition. In this first lecture, we'll cover off the course agenda, intended audience, learning objectives, and course prerequisites. Before we start, I would like to introduce myself. My name is Jeremy Cook, I'm one of the trainers here at Cloud Academy, specializing in AWS. Feel free to connect with either myself or the wider team here at Cloud Academy, regarding anything about this course. You can email us at support@cloudacademy.com, or alternatively, our online community forum is available for your feedback.

This training course begins with an introduction of computer vision and proceeds with the concepts of image and video analysis. We'll then briefly discuss the reasons as to where and when you might consider performing image and video analysis using Amazon Rekognition. Amazon Rekognition makes it easy to add image and video analysis to your applications. You simply submit your image or video to the Rekognition service, and the service will then identify objects, people, text, scenes, emotions and activities. Additionally, the service can be used to moderate content by detecting inappropriate or objectionable content. We'll continue on in the course, providing in-depth reviews of the image, video and collection-based API sets.

We then finish the course with a couple of demonstrations, the first of which will show you how to use Amazon Rekognition together with S3 and CloudFront for hosting, to build a simple facial analysis static web application. In the second demonstration, we will show you how to use the AWS CLI to implement an object and feature detection system that sends and email when certain features are detected within an image. Both demonstrations will highlight the capabilities of Amazon Rekognition and the different approaches you can adopt to interface with the service.

The intended audience for this course includes data scientists interested in mining information from images and/or videos, machine Learning enthusiasts with an interest in computer vision, developers interested in learning how to integrate image and video analysis into their own applications, and anyone interested in learning how Amazon Rekognition works.

By completing this course, you will understand what Amazon Rekognition is and what it offers, understand the benefits of using the Amazon Rekognition service, understand how to use Amazon Rekognition APIs to process both images and videos, understand how to use collections in the storage-based API set, understand business use cases and scenarios that can benefit from using the Amazon Rekognition service, and finally, be able to architect and integrate Amazon Rekognition into your own applications.

The agenda for the remainder of this course is as follows. We will discuss what Amazon Rekognition is and when and why you might consider using it. We'll review the Amazon Rekognition service and provide an in-depth review of each of its features. We'll discuss benefits and business use cases that can be empowered by leveraging Amazon Rekognition. We'll provide some example business use cases and scenarios that utilize the Rekognition service. And finally, we'll present two demonstrations that highlight how to integrate with the Rekognition service.

The following prerequisites will be both useful and helpful for this course. General development and coding experience, AWS, S3, and IAM security experience.

Okay, the course introduction has now been completed, go ahead and close this lecture and we'll see you shortly in the next one where we'll begin discussing the Rekognition service.