Developing AI Applications on Azure from Coursera

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.

What's inside

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides real-world context and application of AI through Microsoft Azure's tools and services, making it easy to apply concepts to practical problems

Covers the fundamentals of AI and ML with a focus on industry-standard Python libraries, providing a solid basis for further learning in machine learning

Integrates Azure Machine Learning Service into the learning experience, enabling students to apply their knowledge in a cloud environment

Offers a variety of hands-on exercises, providing opportunities for students to practice and reinforce their understanding of AI and ML

Suitable for individuals with basic programming skills and an interest in exploring AI and ML, but prior knowledge in AI is not assumed

Reviews summary

Azure ai application development

Learners say this course is largely positive and good for beginners who want a hands-on learning experience. It engaging assignments and covers a wide range of topics. While the instructors are generally well-received, some learners found them to be difficult to follow or unclear. Azure account is required to fully benefit from the course, and Microsoft provides free credits for practice.

Instructors

"The instructor explains everything in a detailed way， even who without any previous programming experience can easily understand the lecture."

"The teacher is extremely competent and clear in explaining even to those, like me, who are not expert on the topic; the course is well structured and provides rich and interesting contents"

"Great course！"

"Excellent, it was a very hands-on course, although in some cases went some fast, no there issue, because the program of study has the time necessary to review the teacher's explanation."

Deployment covered

"Hands on with code description with each and every line was really nice."

"get to learn about how Model build locally and deploy into cloud with azure ML was Very nice ."

Good content

"This course is really helpful for me as I am really interested in developing tools using azure"

"Learnt very happily...!! Understandable and very much useful !!!"

"Wonderful course , learn many unknown thing."

"It's the best knowledge online for me."

"In This course i have learned about artificial intelligence and machine learning..."

Can be difficult for beginners

"The course itself wasn't bad, however it should have been stated that Azure Account is required in order to get the most out of it"

"It is a good summary of Azure options to Artificial Intelligence (machine learning included) but it lacked some sort of "more caution" on details."

"Overall the course content is OK and you can get a grip on some concepts."

"Pros: explores technical details of Azure development tools, not just a high-level overviewuseful Jupyter NB files containing pre-written code for implementing Azure tools, meaning students don't have to begin practicing from scratch"

Could be more clear

"Lacked quality compared to the GCP and AWS courses."

"There were so many errors through the course and so much confusion."

"It is a very good course. Although it is very advanced course. This course requires from you a strong grip on python to understand the concepts otherwise it will be difficult for you."

"The instructor only reads the presentation."

"Very poor attention to learning methods, person is talking fast and reading out simply slides, or maybe its for advance levels."

Azure account is required

"Azure Account is required in order to get the most out of it"

"The course itself wasn't bad, however it should have been stated that Azure Account is required in order to get the most out of it"

Career center

Learners who complete Developing AI Applications on Azure will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine learning engineers are responsible for developing and deploying machine learning models. They work with data scientists to identify problems that can be solved using machine learning, and then they design and build the models that will solve those problems. This course provides a solid foundation in the fundamentals of machine learning, which is essential for machine learning engineers. In addition, the course covers a variety of topics that are relevant to machine learning engineering, such as model training, model evaluation, and model deployment.

See salaries and explore the career path for Machine Learning Engineer

AI Engineer

Artificial intelligence (AI) engineers are responsible for designing and developing AI solutions. They use their knowledge of machine learning, deep learning, and other AI techniques to create systems that can perform tasks that would be difficult or impossible for humans to do. This course provides a solid foundation in the fundamentals of AI and machine learning, which are essential skills for AI engineers. In addition, the course covers a variety of topics that are relevant to AI engineering, such as data preprocessing, model training, and model deployment.

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Research Scientist

Research scientists conduct research in a variety of fields, including AI, machine learning, and data science. They develop new theories and algorithms, and they design and conduct experiments to test their ideas. This course provides a strong foundation in the fundamentals of AI and machine learning, which are essential skills for research scientists. In addition, the course covers a variety of topics that are relevant to research, such as scientific writing and data visualization.

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Data Scientist

Data scientists use their knowledge of statistics, machine learning, and other data analysis techniques to extract insights from data. They work with businesses to identify problems that can be solved using data, and then they develop and implement solutions. This course provides a strong foundation in the fundamentals of machine learning and data analysis, which are essential skills for data scientists. In addition, the course covers a variety of topics that are relevant to data science, such as data visualization, data mining, and data management.

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Software Engineer

Software engineers design, develop, and maintain software systems. They work with businesses to identify problems that can be solved using software, and then they develop and implement solutions. This course provides a solid foundation in the fundamentals of software engineering, which are essential skills for software engineers. In addition, the course covers a variety of topics that are relevant to software engineering, such as software design, software testing, and software deployment.

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Data Analyst

Data analysts use their knowledge of statistics, data analysis techniques, and data visualization to extract insights from data. They work with businesses to identify problems that can be solved using data, and then they develop and implement solutions. This course provides a strong foundation in the fundamentals of data analysis, which are essential skills for data analysts. In addition, the course covers a variety of topics that are relevant to data analysis, such as data visualization, data mining, and data management.

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Technical Writer

Technical writers create documentation for software products. They work with engineers and product managers to create documentation that is clear, concise, and accurate. This course provides a solid foundation in the fundamentals of technical writing, which are essential skills for technical writers. In addition, the course covers a variety of topics that are relevant to technical writing, such as documentation planning, documentation design, and documentation review.

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User Experience Designer

User experience designers are responsible for the design of user interfaces. They work with engineers and product managers to create interfaces that are easy to use and visually appealing. This course provides a solid foundation in the fundamentals of user experience design, which are essential skills for user experience designers. In addition, the course covers a variety of topics that are relevant to user experience design, such as user research, prototyping, and usability testing.

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Sales Engineer

Sales engineers work with customers to identify and sell software products. They help customers understand the benefits of the products and how to use them to solve their business problems. This course provides a solid foundation in the fundamentals of sales engineering, which are essential skills for sales engineers. In addition, the course covers a variety of topics that are relevant to sales engineering, such as product knowledge, sales techniques, and customer relationship management.

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Product Manager

Product managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of customers. This course provides a solid foundation in the fundamentals of product management, which are essential skills for product managers. In addition, the course covers a variety of topics that are relevant to product management, such as market research, product design, and product launch.

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Project Manager

Project managers are responsible for the planning, execution, and closure of projects. They work with teams to develop and implement project plans, and they track progress and ensure that projects are completed on time and within budget. This course provides a solid foundation in the fundamentals of project management, which are essential skills for project managers. In addition, the course covers a variety of topics that are relevant to project management, such as project planning, project scheduling, and project risk management.

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Business Analyst

Business analysts use their knowledge of business processes and data analysis to identify problems that can be solved using technology. They work with businesses to develop and implement solutions that will improve efficiency and profitability. This course provides a solid foundation in the fundamentals of business analysis, which are essential skills for business analysts. In addition, the course covers a variety of topics that are relevant to business analysis, such as data visualization, data mining, and data management.

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Marketing Manager

Marketing managers are responsible for the development and execution of marketing campaigns. They work with teams to create marketing materials, manage social media, and track marketing results. This course provides a solid foundation in the fundamentals of marketing, which are essential skills for marketing managers. In addition, the course covers a variety of topics that are relevant to marketing, such as market research, product positioning, and brand management.

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Consultant

Consultants provide advice and expertise to businesses on a variety of topics, including AI, machine learning, and data science. They work with businesses to identify problems and develop solutions. This course provides a solid foundation in the fundamentals of consulting, which are essential skills for consultants. In addition, the course covers a variety of topics that are relevant to consulting, such as problem solving, communication, and presentation skills.

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Technical Support Engineer

Technical support engineers provide technical support to customers. They help customers troubleshoot problems with software products and provide guidance on how to use the products. This course provides a solid foundation in the fundamentals of technical support, which are essential skills for technical support engineers. In addition, the course covers a variety of topics that are relevant to technical support, such as problem solving, communication, and customer service.

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Developing AI Applications on Azure

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Azure ai application development

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