Microsoft Azure Machine Learning
Learn to create no-code machine learning models using Azure Machine Learning, from automated ML to regression, classification, and clustering.
Learn to create no-code machine learning models using Azure Machine Learning, from automated ML to regression, classification, and clustering.
Machine learning is at the core of artificial intelligence, with many modern applications and services depending on predictive models. This comprehensive course teaches you how to use Azure Machine Learning to create and publish models without writing code. You'll explore automated machine learning to simplify the iterative model training process, and learn to create different types of models including regression for numeric predictions, classification for categorizing data, and clustering for grouping similar entities. Through practical hands-on exercises, you'll create Azure Machine Learning workspaces, explore data, train models, evaluate their performance, and deploy them as services. This course helps prepare you for the AI-900 Microsoft Azure AI Fundamentals certification exam while providing foundational knowledge applicable to other Azure role-based certifications.
4.4
(213 ratings)
18,108 already enrolled
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What you'll learn
Understand core machine learning concepts and common machine learning types
Create and deploy no-code predictive models using Azure Machine Learning
Build regression models to predict numeric values using Azure Machine Learning designer
Develop classification models to categorize data into specific classes
Implement clustering models to group similar entities based on their features
Evaluate model performance and interpret results
Skills you'll gain
This course includes:
0.3 Hours PreRecorded video
2 quizzes, 10 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
This course provides a comprehensive introduction to Azure Machine Learning, focusing on creating models without writing code. Students learn the fundamentals of machine learning concepts and how to apply them using Azure's designer interface. The curriculum is structured around four main machine learning techniques: automated machine learning, regression, classification, and clustering. For each technique, students complete hands-on exercises that walk through the entire ML workflow - from setting up an Azure Machine Learning workspace to deploying predictive services. The exercises follow a consistent pattern of creating resources, exploring data, building and running training pipelines, evaluating models, and deploying them as services. This practical approach ensures students gain experience with the complete machine learning lifecycle in Azure.
Use Automated Machine Learning in Azure Machine Learning
Module 1 · 3 Hours to complete
Create a Regression Model with Azure Machine Learning Designer
Module 2 · 2 Hours to complete
Create a Classification Model with Azure AI
Module 3 · 2 Hours to complete
Create a Clustering Model with Azure AI
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Empowering Individuals and Organizations Through Technology
Microsoft's mission is "to empower every person and every organization on the planet to achieve more," reflecting its commitment to leveraging technology for global empowerment. The company aims to drive digital transformation through an integrated cloud approach, creating a robust platform that enhances productivity and accessibility for users worldwide. Its vision statement emphasizes the goal of democratizing artificial intelligence, ensuring that AI technologies are accessible and beneficial for everyone. This focus on empowerment and inclusivity underpins Microsoft's strategies and product development, positioning it as a leader in innovation within the technology sector. The company has consistently pursued excellence by fostering a culture of innovation, diversity, and corporate social responsibility, ultimately aiming to improve quality of life and facilitate progress across various fields, including education, healthcare, and business.
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4.4 course rating
213 ratings
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