Microsoft Azure Machine Learning from Coursera

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code.

This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the second course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains. This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them.

This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial. To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology, general technology concepts, including concepts of machine learning and artificial intelligence.

What's inside

Syllabus

Use Automated Machine Learning in Azure Machine Learning

Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this module, you'll learn how to identify different kinds of machine learning model and how to use the automated machine learning capability of Azure Machine Learning to train and deploy a predictive model.

Create a Regression Model with Azure Machine Learning Designer

Regression is a supervised machine learning technique used to predict numeric values. in this module, you will learn how to create regression models using Azure Machine Learning designer.

Create a Classification Model with Azure AI

Classification is a supervised machine learning technique used to predict categories or classes. In this module, you will learn how to create classification models using Azure Machine Learning designer.

Create a Clustering Model with Azure AI

Clustering is an unsupervised machine learning technique used to group similar entities based on their features. In this module, you will learn how to create clustering models using Azure Machine Learning designer.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for beginners with limited programming knowledge who seek a grasp of Azure Machine Learning's capabilities

Demonstrates automated machine learning techniques, a valuable skill in streamlining model development

Provides hands-on experience in creating regression, classification, and clustering models using Azure Machine Learning Designer

Teaches fundamental machine learning concepts and skills relevant to the AI fundamentals exam

Part of a larger program of courses for Microsoft Azure AI certification

Assumes basic computer and English proficiency

Reviews summary

Mixed azure machine learning reviews

Learners say that this course provides a solid introduction to Azure Machine Learning. Students remark that the course structure is well-organized. However, some note that course materials may be outdated or inaccurate.

Good starting point for beginners.

"Good starting point"

"beginner friendly but have to make sure that you posses fundamental statistical knowledge"

Organized layout with clear instructions.

"The course structure is great and is laid out with clear step-by-step instructions."

"This course need to be rework as Azure has changed/ modified its GUI a lot, otherwise it is very good."

Outdated information and incorrect details can lead to confusion.

"All of the exercises are several years out of date"

"outdated instructions and many links not working"

"I had to manually write the codes in order for deployment to work."

Hands-on exercises may be confusing due to outdated instructions.

"Many sections of the course provide the bare minimum instruction for the hands on exercise."

"A lot of copy-n-pasted parts between each of 4 weeks."

"Errors in python code parts"

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 Microsoft Azure Machine Learning with these activities:

Organize and Review Course Materials

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Actively engage with course materials to enhance your understanding and recall.

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Review lecture notes, slides, and videos.
Take notes and summarize key concepts.
Complete assignments and quizzes.
Participate in class discussions.
Ask questions to clarify your understanding.

Review basic programming concepts

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Strengthen your programming foundation for a smoother learning experience in Azure Machine Learning.

Browse courses on Programming Fundamentals

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Review basic data types and their operations
Practice writing simple programs using variables and control flow

Join a study group or online forum for Azure Machine Learning

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Connect with fellow learners and engage in discussions to enhance your understanding of Azure Machine Learning.

Browse courses on Peer Support

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Identify or join an online forum or study group focused on Azure Machine Learning
Participate in discussions, ask questions, and share your knowledge
Collaborate on projects or learning exercises

Ten other activities

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Attend Machine Learning Meetups and Conferences

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Expand your network, learn from industry experts, and stay updated on the latest machine learning advancements.

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Identify relevant machine learning meetups and conferences.
Register and attend the events.
Network with other attendees, including researchers, practitioners, and industry leaders.
Attend presentations and workshops to learn about the latest trends and developments.
Share your own knowledge and insights.

Compile resources on Azure Machine Learning

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Expand your knowledge base by gathering a collection of useful resources related to Azure Machine Learning.

Browse courses on Machine Learning Tools

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Search for articles, tutorials, and documentation on Azure Machine Learning
Bookmark or save relevant resources for future reference
Share your compilation with other learners

Follow tutorials on Azure Machine Learning documentation

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Expand your knowledge of Azure Machine Learning and reinforce the concepts covered in the course.

Browse courses on Machine Learning Fundamentals

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Visit the Azure Machine Learning documentation website
Choose a tutorial that aligns with your learning goals
Follow the step-by-step instructions to complete the tutorial

Follow Machine Learning Tutorials and Courses

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Supplement your learning by exploring additional resources and guided tutorials.

Browse courses on Machine Learning Fundamentals

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Identify reputable machine learning tutorials and courses.
Follow the tutorials and courses, completing the exercises and assignments.
Apply what you learn in your own machine learning projects.
Share your knowledge and insights with others.
Join online forums and communities to connect with other learners and experts.

Practice creating and deploying machine learning models with Azure Machine Learning

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Reinforce your understanding of the machine learning model creation and deployment process.

Browse courses on Machine Learning Models

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Create a new machine learning project in Azure Machine Learning
Choose a dataset and prepare it for training
Select a machine learning algorithm and train a model
Evaluate the model's performance
Deploy the model to Azure Machine Learning

Practice Machine Learning Problems

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Concrete the concepts of machine learning by repeatedly applying them in practical scenarios.

Browse courses on Machine Learning Fundamentals

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Identify a machine learning problem to solve.
Gather and prepare the necessary data.
Build a machine learning model.
Evaluate the model's performance.
Deploy the model and monitor its performance.

Build a machine learning model using Azure Machine Learning

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Apply your learning by building a practical machine learning model that addresses a real-world problem.

Browse courses on Machine Learning Model

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Identify a problem or use case that can be solved using machine learning
Gather and prepare a dataset
Select and train a machine learning model
Evaluate the model's performance
Deploy the model to Azure Machine Learning

Contribute to Open Source Machine Learning Projects

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Immerse yourself in the machine learning community and gain practical experience by contributing to open source projects.

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Identify open source machine learning projects that align with your interests.
Read the project documentation and code.
Identify areas where you can contribute.
Make your contributions to the project.
Collaborate with other contributors and project maintainers.

Build a Machine Learning Application

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Gain hands-on experience in applying machine learning to solve real-world problems.

Browse courses on Machine Learning Applications

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Define the problem and scope of the project.
Gather and prepare the necessary data.
Build and train a machine learning model.
Deploy the model and monitor its performance.
Write a report or presentation on the project.

Develop a Machine Learning White Paper

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Synthesize your knowledge of machine learning by writing a comprehensive report on a specific topic.

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Choose a topic for the white paper.
Research the topic and gather relevant information.
Write the white paper, ensuring it is well-organized and well-written.
Proofread and edit the white paper.
Publish or share the white paper.

Career center

Learners who complete Microsoft Azure Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use machine learning to build models and research trends on big data. This course would be of significant use to a Data Scientist because it gives an opportunity to get hands-on experience with Azure Machine Learning, which Data Scientists frequently use.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

An Artificial Intelligence Engineer needs a foundation in working with machine learning models and how they can predict and support downstream activities. This course dives into how to work with Azure Machine Learning, giving future and current AI engineers an opportunity to enhance their skillset.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

The main responsibility that a Machine Learning Engineer has is developing and deploying machine learning models. Due to the fact that this course provides hands-on experience with how to work with Azure Machine Learning to create predictive models, it could be helpful to a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

A data engineer could benefit from learning how to build and deploy machine learning models. This course focuses on Azure Machine Learning, a cloud-based platform for building and deploying machine learning models.

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Business Intelligence Analyst

Business Intelligence Analysts help to use past data to help make better informed decisions. This course in Azure Machine Learning may be helpful in performing this task, as it would allow them to become familiar with how to use machine learning to forecast outcomes in their business.

See salaries and explore the career path for Business Intelligence Analyst

Data Architect

Data Architects design and build the infrastructure needed for data to be processed and used in an organization. Machine learning is an essential piece of this infrastructure and this course provides a basis for working with machine learning within the Azure Machine Learning framework.

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Software Engineer

A Software Engineer interested in developing and using machine learning models may find this course to be a good way to do it. Azure Machine learning is used to train and deploy models. Someone looking to build their skills in these areas would be wise to take the course.

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Cloud Architect

Cloud Architects could find this course to be useful as they navigate designing and building cloud-based architectures. Azure Machine Learning is a popular tool for building and deploying machine learning models in the cloud, so this course could provide valuable insights for Cloud Architects.

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IT Consultant

An IT Consultant may find it helpful to take this course in order to gain knowledge of how to use Azure Machine Learning. This would allow them to better advise clients on how to use machine learning to improve their businesses.

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Project Manager

Project Managers may find that this course helps them stay up-to-date with the latest trends in machine learning. By learning how to use Azure Machine Learning, Project Managers can be better prepared to manage projects that involve machine learning.

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Technical Writer

Technical writers may find this course helpful in understanding machine learning concepts. By learning how to use Azure Machine Learning, Technical Writers can be better prepared to write documentation for machine learning products and services.

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Product Manager

This course may be of some use to a Product Manager seeking to develop new machine learning-based products. By learning how to use Azure Machine Learning, Product Managers can gain a better understanding of the capabilities and limitations of machine learning.

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Instructional Designer

Instructional Designers could take this course to learn about machine learning and how it can be used in education. By learning how to use Azure Machine Learning, Instructional Designers can be better prepared to create learning materials that incorporate machine learning.

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Database Administrator

A database administrator can use this course to increase their knowledge of how to apply machine learning to data. Azure Machine Learning is used to create and deploy models, which may be helpful for database administrators looking to advance their career in this area.

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Data Analyst

Analyzing large datasets is a primary function of Data Analysts. Being able to use a range of methods to gain insights from data is a requirement for the role. This course may be of interest to you due to its focus on using Automated Machine Learning in Azure Machine Learning to train and deploy a predictive model.

See salaries and explore the career path for Data Analyst