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Course Outline
Introduction
Azure Machine Learning Overview
- What is Azure Machine Learning?
- Azure Machine learning features
- Azure Machine Learning architecture
Preparing the Machine Learning Operations Environment
- Setting up Azure Machine Learning lab environment
Data Processing
- Importing and unzipping data and datasets
- Transforming and cleaning data
- Separating training data and test data
Classifications and Regressions
- Creating binary and multi-binary models
- Working with regression models
- Tuning hyperparameters and parameters
- Implementing predictive and impact analysis
- Building decision trees and decision forests
Clustering
- Implementing cluster analysis
NLP
- Featuring and labeling data
- Using text analysis
Recommender Systems
- Working with Matchbox Recommender models
Deployment
- Creating, exposing, and consuming machine learning model web services
Summary and Conclusion
Requirements
- Experience with the Azure cloud platform
Audience
- Data Scientists
14 Hours
Testimonials (5)
It was very much what we asked for—and quite a balanced amount of content and exercises that covered the different profiles of the engineers in the company who participated.
Arturo Sanchez - INAIT SA
Course - Microsoft Azure Infrastructure and Deployment
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
Course - Architecting Microsoft Azure Solutions
The Exercises
Khaled Altawallbeh - Accenture Industrial SS
Course - Azure Machine Learning (AML)
very friendly and helpful
Aktar Hossain - Unit4
Course - Building Microservices with Microsoft Azure Service Fabric (ASF)
The practical part, I was able to perform exercises and to test the Microsoft Azure features
