Azure Machine Learning training

This module introduces machine learning and discussed how algorithms and languages are used.

• What is machine learning?
• Introduction to machine learning algorithms
• Introduction to machine learning languages

• Sign up for Azure machine learning studio account
• View a simple experiment from gallery
• Evaluate an experiment

• Describe machine learning
• Describe machine learning algorithms
• Describe machine learning languages

Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.

• Azure machine learning overview
• Introduction to Azure machine learning studio
• Developing and hosting Azure machine learning applications

• Explore the Azure machine learning studio workspace
• Clone and run a simple experiment
• Clone an experiment, make some simple changes, and run the experiment

• Describe Azure machine learning.
• Use the Azure machine learning studio.
• Describe the Azure machine learning platforms and environments.

At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.

• Categorizing your data
• Importing data to Azure machine learning
• Exploring and transforming data in Azure machine learning

• Prepare Azure SQL database
• Import data
• Visualize data
• Summarize data

• Understand the types of data they have.
• Upload data from a number of different sources.
• Explore the data that has been uploaded.

This module provides techniques to prepare datasets for use with Azure machine learning.

• Data pre-processing
• Handling incomplete datasets

• Explore some data using Power BI
• Clean the data

• Pre-process data to clean and normalize it.
• Handle incomplete datasets.

This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.

• Using feature engineering
• Using feature selection

• Prepare datasets
• Use Join to Merge data

• Use feature engineering to manipulate data.
• Use feature selection.

This module describes how to use regression algorithms and neural networks with Azure machine learning.

• Azure machine learning workflows
• Scoring and evaluating models
• Using regression algorithms
• Using neural networks

• Using Azure machine learning studio modules for regression
• Create and run a neural-network based application

• Describe machine learning workflows.
• Explain scoring and evaluating models.
• Describe regression algorithms.
• Use a neural-network.

This module describes how to use classification and clustering algorithms with Azure machine learning.

• Using classification algorithms
• Clustering techniques
• Selecting algorithms

• Using Azure machine learning studio modules for classification.
• Add k-means section to an experiment
• Add PCA for anomaly detection.
• Evaluate the models

• Use classification algorithms.
• Describe clustering techniques.
• Select appropriate algorithms.

This module describes how to use R and Python with azure machine learning and choose when to use a particular language.

• Using R
• Using Python
• Incorporating R and Python into Machine Learning experiments

• Exploring data using R
• Analyzing data using Python

• Explain the key features and benefits of R.
• Explain the key features and benefits of Python.
• Use Jupyter notebooks.
• Support R and Python.

This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.

• Using hyper-parameters
• Using multiple algorithms and models
• Scoring and evaluating Models

• Using hyper-parameters

• Use hyper-parameters.
• Use multiple algorithms and models to create ensembles.
• Score and evaluate ensembles.

This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.

• Deploying and publishing models
• Consuming Experiments

• Deploy machine learning models
• Consume a published model

• Deploy and publish models.
• Export data to a variety of targets.

This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.

• Cognitive services overview
• Processing language
• Processing images and video
• Recommending products

• Build a language application
• Build a face detection application
• Build a recommendation application

• Describe cognitive services.
• Process text through an application.
• Process images through an application.
• Create a recommendation application.

This module describes how use HDInsight with Azure machine learning.

• Introduction to HDInsight
• HDInsight cluster types
• HDInsight and machine learning models

• Provision an HDInsight cluster
• Use the HDInsight cluster with MapReduce and Spark

• Describe the features and benefits of HDInsight.
• Describe the different HDInsight cluster types.
• Use HDInsight with machine learning models.

This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.

• R and R server overview
• Using R server with machine learning
• Using R with SQL Server

• Deploy DSVM
• Prepare a sample SQL Server database and configure SQL Server and R
• Use a remote R session
• Execute R scripts inside T-SQL statements

• Implement interactive queries.
• Perform exploratory data analysis.