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Online Course

Developing AI Applications on Azure

LearnQuest via Coursera

Overview

This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning.

Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach. As you go through the course, we'll introduce you to Microsoft's pre-trained and managed machine learning offered as REST API's in their suite of cognitive services. We'll implement solutions using the computer vision API and the facial recognition API, and we'll do sentiment analysis by calling the natural language service.

Using the Azure Machine Learning Service you'll create and use an Azure Machine Learning Worksace.Then you'll train your own model, and you'll deploy and test your model in the cloud. Throughout the course you will perform hands-on exercises to practice your new AI skills. By the end of this course, you will be able to create, implement and deploy machine learning models.

Syllabus

Introduction to Artificial Intelligence
-This module introduces Artificial Intelligence and Machine learning. Next, we talk about machine learning types and tasks. This leads into a discussion of machine learning algorithms. Finally we explore python as a popular language for machine learning solutions and share some scientific ecosystem packages which will help you implement machine learning. By the end of this unit you will be able to implement machine learning models in at least one of the available python machine learning libraries.

Standardized AI Processes and Azure Resources
-This module introduces machine learning tools available in Microsoft Azure. It then looks at standardized approaches developed to help data analytics projects to be successful. Finally, it gives you specific guidance on Microsoft's Team Data Science Approach to include roles and tasks involved with the process. The exercise at the end of this unit points you to Microsoft's documentation to implement this process in their DevOps solution if you don't have your own.

Azure Cognitive APIs
-This module introduces you to Microsoft's pretrained and managed machine learning offered as REST API's in their suite of cognitive services. We specifically implement solutions using the computer vision api, the facial recognition api, and do sentiment analysis by calling the natural language service.

Azure Machine Learning Service: Model Training
-This module introduces you to the capabilities of the Azure Machine Learning Service. We explore how to create and then reference an ML workspace. We then talk about how to train a machine learning model using the Azure ML service. We talk about the purpose and role of experiments, runs, and models. Finally, we talk about
Azure resources available to train your machine learning models with. Exercises in this unit include creating a workspace, building a compute target, and executing a training run using the Azure ML service.

Azure Machine Learning Service: Model Management and Deployment
-This module covers how to connect to your workspace. Next, we discuss how the model registry works and how to register a trained model locally and from a workspace training run. In addition, we show you the steps to prepare a model for deployment including identifying dependencies, configuring a deployment target, building a container image. Finally, we deploy a trained model as a webservice and test it by sending JSON objects to the API.

Taught by

Ronald J. Daskevich, DCS

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Reviews

4.8 rating, based on 4 reviews

Start your review of Developing AI Applications on Azure

  • Surya is taking this course right now.

    I hope that this course would give me a better understanding and thinking ability so that i can write programs of artificial intelligence and develop a software to
  • Diego H.

    Diego completed this course, spending 3 hours a week on it and found the course difficulty to be easy.

    A concise and straighforward introduction to Azure ML services. There is no a theorical introduction to Machine Learning on this course, its focus is on practice.
  • Hina is taking this course right now and found the course difficulty to be medium.

    Good evening,
    Sir/Madam

    I am really interested to know about artificial intelligence. I heard this AI word in seminar since from that time I m searching this course.
    Finally I found this course....
    I m waiting when I m going to take my class
    I hope I m going to get a good knowledge from this course and if I like this field then I will start my career in this.

    I hope you will provide me your best support.

    Thanking you
  • Profile image for Jorge P
    Jorge P.

    Jorge completed this course.

    The course is easy to follow the instructor explains everything very well! Although his pace is a little fast, he is still understandable and therefore making this course worth your time!!!

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