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Course Outline
Introduction
Overview of Azure Machine Learning
- What is 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.
- Dividing data into training and test sets.
Classifications and Regressions
- Developing binary and multi-class models.
- Working with regression models.
- Tuning hyperparameters and model parameters.
- Implementing predictive and impact analysis.
- Constructing decision trees and decision forests.
Clustering
- Implementing cluster analysis.
NLP
- Feature extraction and labeling of 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.
