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Microsoft Azure Machine Learning

Microsoft

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.

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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.

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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.
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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

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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

Coming soon We're preparing activities for Microsoft Azure Machine Learning. These are activities you can do either before, during, or after a course.

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected ten 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 Microsoft Azure Machine Learning.
Provides a comprehensive introduction to machine learning with Python, covering fundamental concepts, algorithms, and practical applications. It valuable resource for beginners and those looking to expand their knowledge of machine learning.
Provides a comprehensive overview of deep learning concepts and techniques using Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of reinforcement learning concepts and techniques. It covers a wide range of topics, including Markov decision processes, value functions, and policy optimization.
Provides a comprehensive overview of natural language processing concepts and techniques using Python. It covers a wide range of topics, including text preprocessing, feature engineering, and model selection.
Provides a comprehensive overview of machine learning algorithms. It covers a wide range of topics and offers practical examples and exercises, making it a valuable resource for those interested in understanding the underlying principles of machine learning algorithms.
While this book focuses on deep learning, it provides a solid foundation in machine learning concepts and algorithms. It valuable resource for those interested in exploring advanced machine learning techniques.
Provides a gentle introduction to machine learning for absolute beginners. It covers fundamental concepts and practical applications, making it a valuable resource for those looking to build a foundation in machine learning.
Provides a comprehensive overview of machine learning concepts and techniques for beginners. It will help students understand the basics of machine learning and how to apply them to practical problems.

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