Have you ever wondered how computers are able to understand and interact with human language? Well, that’s where Natural Language Processing (NLP) comes into play. NLP is a branch of artificial intelligence (AI) that allows computer programs to comprehend and respond to language just as humans do. It is the technology behind digital assistants, speech-to-text tools, and customer service chatbots.
Microsoft Azure, a leading cloud computing platform, makes it incredibly simple to build applications with natural language processing capabilities. They offer a range of services including text analytics, translation, and language understanding. One of these services is the Language Understanding service, which allows developers to create language models for their applications. If you’re interested in learning more about this service, you can complete the Create a Language Understanding solution learning path in the Microsoft AI School.
Before starting this learning path, it is recommended that you have some familiarity with Microsoft Azure and basic programming skills in either C# or Python. If you’re new to programming, don’t worry! You can first complete the Take your first steps with C# or Take your first steps with Python learning paths.
This learning path consists of three modules, each covering different aspects of natural language processing. Let’s take a brief look at what each module entails.
Module 1: Building Language Models
In this module, you’ll learn how to provision Azure resources for the Language Understanding service and create language models. You’ll delve into concepts like defining utterances and intents, defining entities, using patterns to differentiate similar utterances, using pre-built models, and training, testing, publishing, and reviewing a Language Understanding application.
Module 2: Publishing and Deployment
Once you’ve built your language understanding application, you need to publish and consume it from client applications. This module will teach you how to set publishing configuration options, describe language understanding prediction results, and deploy the Language Understanding service as a container using technologies like Docker and Kubernetes.
Module 3: Integration with Speech Services
In this final module, you’ll learn how to integrate the Language Understanding service with Speech services to enable intent recognition from spoken inputs. This will involve using the Speech SDK and enabling the Speech priming publishing setting.
By completing this learning path, you’ll gain the knowledge and skills to implement natural language processing solutions using Azure’s Language Understanding service. You’ll be able to create language models and integrate them with other services to build applications that can understand and respond to human language.
If you’re eager to master the concepts and techniques of Natural Language Processing and want to enhance your applications with the ability to understand and analyze written or spoken language, the Microsoft AI School’s learning path on Language Understanding is the perfect resource for you. And don’t forget, Skrots also provides similar services. You can learn more by visiting https://skrots.com. Check out all the services they offer at https://skrots.com/services.