Time (min) | Activity |
---|---|
50 | Introduction to Machine Learning |
20 | Lab1 - Setting up development environment |
45 | Lab2 - Introduction to R, Python & Data Synth |
45 | Lab3 - AzureML Experiments & Data Interaction |
60 | Lab4 - Develop and Consume AzureML Models |
45 | Lab5 - Custom Scripts (R & Python) in AML |
60 | Lab6 - Evaluate model performance in AML |
60 | Lab7 - Azure ML Batch Score, Retrain, Production and Automatization |
-
Setting up development environment
- Overview
- Objectives
- Requirements
- Create free tier Azure ML account
- Create standard tier Azure ML account
- Install R and R Studio
- Install Anaconda Python
- Overview
-
Introduction to R, Python & Data Synth
- Overview
- Objectives
- Requirements
- Generate Synthetic Data
- Microsoft Excel
- R
- Python
- Microsoft Azure SQL Server
- Microsoft Azure Blob Storage
- Other Dataset sources
- Overview
-
AzureML Experiments & Data Interaction
- Overview
- Objectives
- Requirements
- Creating AzureML Experiment
- Accessing Data
- Access data, use existing dataset
- Upload your own dataset
- Upload your own compressed dataset
- Manually enter data
- Access data on Azure Storage
- Access data on Azure SQL Database
- Overview
-
Develop and Consume AzureML Models
- Overview
- Objectives
- Requirements
- Working with AzureML Models
- Training a model
- Publishing a trained model as Web Service
- Removing Web Service Redundant input & output parameters
- Consume the ML Web Service in a C# application
- Input data type
- Overview
-
Custom Scripts (R & Python) in AML
- Overview
- Objectives
- Requirements
- R & Python Script Modules
- Using Execute R Script module
- Using Python Script module
- R & Python compatibility with Azure ML
- Overview
-
Evaluate model performance in AML
- Overview
- Objectives
- Requirements
- Performance evaluation
- Splitting data
- Scoring the model
- Evaluate a Regression model
- Evaluate more than one model
- Cross Validation
- Performance evaluation (cont.)
- Evaluate a Binary classification model
- Comparing two binary classification model
- Cross Validation on Binary Classification
- Evaluating a Multi-class classification model
- Feature engineering
- Which feature is or is not important?
- Simpler method to measure a feature’s importance
- Overview
-
Azure ML Batch Score, Retrain, Production and Automatization
- Overview
- Objectives
- Requirements
- Importance of Retraining, seeing the whole picture
- Batch and Request/Response scoring web services
- Stages to create a scoring web service
- Request/Response Service (RRS)
- Batch Execution Service (BES)
- Web Service Input/Output Parameter alternatives
- Stages to create a scoring web service
- Azure ML Retraining
- Overview