We may earn an affiliate commission when you visit our partners.
Ronald J. Daskevich, DCS
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/w7pz5o/developing
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.
Read more
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.
Read more
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
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
Save this course
Save Developing AI Applications on Azure to your list so you can find it easily later:
Save
Save
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"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Developing AI Applications on Azure with these
activities:
Review Python
Show steps
Prepare for the course by reviewing basics of Python, which will help you excel in subsequent modules.
Browse courses on
Python
Show steps
-
Review Python syntax and data structures
-
Practice writing simple Python programs
Host a Study Group
Show steps
Enhance your understanding of course concepts by discussing with peers, clarifying doubts, and sharing knowledge.
Show steps
-
Organize a study group with fellow students
-
Choose a topic to discuss
-
Facilitate the discussion and encourage participation
Practice Machine Learning Algorithms
Show steps
Solidify your understanding of machine learning algorithms by solving practice problems and coding solutions.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a machine learning algorithm (e.g., linear regression, decision tree)
-
Solve practice problems using the chosen algorithm
-
Implement the algorithm in Python
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Azure Machine Learning Service
Show steps
Gain hands-on experience with Azure Machine Learning Service through guided tutorials, which will prepare you for deploying models later in the course.
Browse courses on
Azure Machine Learning Service
Show steps
-
Find Azure Machine Learning Service tutorials
-
Follow the tutorials to create and deploy a machine learning model
Mentor Junior Machine Learning Enthusiasts
Show steps
Solidify your understanding of course concepts by mentoring junior machine learning enthusiasts, who can benefit from your guidance and expertise.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor junior machine learning enthusiasts
-
Provide guidance and support on machine learning concepts and projects
Contribute to Open Source Machine Learning Projects
Show steps
Gain real-world experience by contributing to open source machine learning projects, which will expose you to best practices and the latest advancements in the field.
Browse courses on
Open Source
Show steps
-
Identify open source machine learning projects to contribute to
-
Choose a contribution, such as bug fixes, feature additions, or documentation improvements
-
Submit your contribution and engage with the project community
Develop a Machine Learning Project
Show steps
Apply your skills by developing a complete machine learning project, which will provide valuable experience in the entire workflow.
Show steps
-
Define the problem and gather data
-
Choose and train a machine learning model
-
Evaluate and deploy the model
Review Python
Show steps
Prepare for the course by reviewing basics of Python, which will help you excel in subsequent modules.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data structures
- Practice writing simple Python programs
Host a Study Group
Show steps
Enhance your understanding of course concepts by discussing with peers, clarifying doubts, and sharing knowledge.
Show steps
Show steps
Show steps
- Organize a study group with fellow students
- Choose a topic to discuss
- Facilitate the discussion and encourage participation
Practice Machine Learning Algorithms
Show steps
Solidify your understanding of machine learning algorithms by solving practice problems and coding solutions.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a machine learning algorithm (e.g., linear regression, decision tree)
- Solve practice problems using the chosen algorithm
- Implement the algorithm in Python
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Azure Machine Learning Service
Show steps
Gain hands-on experience with Azure Machine Learning Service through guided tutorials, which will prepare you for deploying models later in the course.
Browse courses on
Azure Machine Learning Service
Show steps
Show steps
Show steps
- Find Azure Machine Learning Service tutorials
- Follow the tutorials to create and deploy a machine learning model
Mentor Junior Machine Learning Enthusiasts
Show steps
Solidify your understanding of course concepts by mentoring junior machine learning enthusiasts, who can benefit from your guidance and expertise.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior machine learning enthusiasts
- Provide guidance and support on machine learning concepts and projects
Contribute to Open Source Machine Learning Projects
Show steps
Gain real-world experience by contributing to open source machine learning projects, which will expose you to best practices and the latest advancements in the field.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open source machine learning projects to contribute to
- Choose a contribution, such as bug fixes, feature additions, or documentation improvements
- Submit your contribution and engage with the project community
Develop a Machine Learning Project
Show steps
Apply your skills by developing a complete machine learning project, which will provide valuable experience in the entire workflow.
Show steps
Show steps
Show steps
- Define the problem and gather data
- Choose and train a machine learning model
- Evaluate and deploy the model
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 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.
AI 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.
Research Scientist
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.
Data Scientist
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.
Software Engineer
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.
Data Analyst
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.
Technical Writer
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.
User Experience Designer
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.
Sales Engineer
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.
Product Manager
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.
Project Manager
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.
Business Analyst
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.
Marketing Manager
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.
Consultant
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.
Technical Support Engineer
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.
For more career information including salaries, visit:
OpenCourser.com/course/w7pz5o/developing
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Developing AI Applications on Azure.
Save
Comprehensive guide to deep learning for practitioners. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It also includes a number of hands-on exercises.
Practical guide to machine learning for beginners. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation. It also includes a number of hands-on exercises.
Comprehensive guide to machine learning with Python for practitioners. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It also includes a number of hands-on exercises.
Comprehensive guide to machine learning with Java for practitioners. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It also includes a number of hands-on exercises.
Comprehensive guide to machine learning with R for practitioners. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation. It also includes a number of hands-on exercises.
Comprehensive guide to machine learning with Go for practitioners. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It also includes a number of hands-on exercises.
Good introduction to artificial intelligence and machine learning for beginners. It covers a wide range of topics, including machine learning algorithms, natural language processing, computer vision, and robotics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w7pz5o/developing
Share
Similar courses
Here are nine courses similar to
Developing AI Applications on Azure.
AI Fundamentals
OpenCourser.com/course/xwscz2/ai
AI Fundamentals
Most relevant
Optimizing Microsoft Azure AI Solutions
OpenCourser.com/course/4q4u2f/optimizing
Optimizing Microsoft Azure AI Solutions
Most relevant
Introduction to the Configuring and Operating Microsoft Azure Virtual Desktop (AZ-140) Exam
OpenCourser.com/course/z8up7x/introduction
Introduction to the Configuring and Operating Microsoft...
Most relevant
Getting Started with Azure Machine Learning Studio
OpenCourser.com/course/go1rmi/getting
Getting Started with Azure Machine Learning Studio
Most relevant
Creating & Deploying Microsoft Azure Machine Learning Studio Solutions
OpenCourser.com/course/bsvnj8/creating
Creating & Deploying Microsoft Azure Machine Learning...
Most relevant
Monitoring Azure Resources and Web Applications with System Center Operations Manager 2016 (SCOM)
OpenCourser.com/course/03s1lz/monitoring
Monitoring Azure Resources and Web Applications with...
Most relevant
Introduction to MLOps on Azure
OpenCourser.com/course/tuc7og/introduction
Introduction to MLOps on Azure
Designing Machine Learning Solutions on Microsoft Azure
OpenCourser.com/course/k4wmhn/designing
Designing Machine Learning Solutions on Microsoft Azure
Mathematics for Machine Learning: PCA
OpenCourser.com/course/cg4mll/mathematics
Mathematics for Machine Learning: PCA
Level
Advanced
Via
Coursera
Institution
LearnQuest
Instructor
Ronald J. Daskevich, DCS
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
Good to know
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
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser