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Course Outline
Introduction
Azure Machine Learning Overview
- What is Azure Machine Learning?
- Key features of Azure Machine Learning
- Architecture of Azure Machine Learning
Preparing the Machine Learning Operations Environment
- Setting up 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
- Creating binary and multi-class models
- Working with regression models
- Tuning hyperparameters and model parameters
- Implementing predictive and impact analysis
- Building decision trees and random forests
Clustering
- Implementing cluster analysis
Natural Language Processing (NLP)
- Featuring and labeling data
- Utilizing 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 (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.
