You may have come across some of the popular buzzwords going around in the field of computer science such as machine learning and artificial intelligence (AI). As the world of computing is progressing, humans are now prone to delegate many of their tasks to machines. Machine learning and AI are making computers perform tasks that human intelligence can do. In this article, I am going to give you a brief introduction to machine learning and then we will take a deep dive into Azure Cognitive Services. I will also introduce you to the list of Cognitive Services APIs that can be used to solve business problems.

Today in class, I taught some fundamentals of API consumption in R. As it was aligned to some Microsoft content, we first used HaveIBeenPwned.com's API and then played with Microsoft Cognitive Services' Text Analytics API. This brief post overviews what you need to get started, and how you can chain consecutive calls to these APIs in order to perform multi-lingual sentiment analysis. Get the key by signing up for free on the Cognitive Services site for the Text Analytics API. Make sure to verify your email address!

Microsoft Cognitive Services is a set of cloud-based machine-intelligence APIs that you can use to extract structured data from complex sources (unstructured text, images, video and audio), and add "AI" type features to applications. A good example is the "Seeing AI" glasses in the video below: the image descriptions, emotion inference, and text recognition are all driven by Cognitive Services APIs. You can call these APIs from any application, including R. But since every Cognitive Service API is just a standard REST API, we can use the httr package to call the API. Input and output is standard JSON, which we can create and extract using the jsonlite package. And there are already Python SDKs for many of the services, including the Face API.) Shohbit has also published a step-by-step guide to calling Cognitive Services APIs from R, with several examples of using the Text Analytics API.

After we Save and Activate our process, we are now ready to test it.

This post is authored by Ollie Newth, Program Manager at Microsoft. Although text often contains highly valuable data for companies, extracting meaningful data from it can be a challenge. The field of text analytics utilizes natural language processing to extract meaningful structured data from text, and often includes areas such as sentiment analysis, entity recognition and linking, and text clustering. The Microsoft Text Analytics API is one of the Cognitive Services that can help you turn unstructured text into meaningful insights. The API is one part of the Cortana Intelligence Suite, a family of services that helps enterprises build large-scale analytics solutions.

Imagine you want to know the sentiment of an email without reading it. This can be useful if you want to ignore negative emails for certain parts of the day. I have built a solution for that, by integrating Azure Machine Learning in an Office Outlook Add-In. I had already planned this blog post a month ago, but at that time it was not working in client-side only applications; the API end point was not CORS enabled. This has now been fixed by Microsoft, so I can use this from my Outlook Add-In without any server side component.