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Course Outline
Introduction
Overview of Azure Machine Learning
- Understanding Azure Machine Learning
- Key features of Azure Machine Learning
- Architecture of Azure Machine Learning
Setting Up the Machine Learning Operations Environment
- Configuring the Azure Machine Learning lab environment
Data Processing
- Importing and extracting data and datasets
- Transforming and cleaning data
- Splitting data into training and test sets
Classifications and Regressions
- Developing binary and multi-class models
- Working with regression models
- Tuning hyperparameters and parameters
- Implementing predictive and impact analysis
- Building decision trees and decision forests
Clustering
- Conducting cluster analysis
Natural Language Processing (NLP)
- Feature engineering and labeling data
- Utilizing text analysis techniques
Recommender Systems
- Working with Matchbox Recommender models
Deployment
- Creating, exposing, and consuming machine learning model web services
Summary and Conclusion
Requirements
- Prior experience with the Azure cloud platform
Target Audience
- Data Scientists
14 Hours
Testimonials (3)
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
The course, Trainer
Novat Adam - Tanzania Revenue Authority
Course - Architecting Microsoft Azure Solutions
The details and the presentation style.
