Perform Cloud Data Science with Azure Machine Learning (20774)

Module 1: Introduction to Machine Learning

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

Lessons

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

Lab : Introduction to machine Learning

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

After completing this module, students will be able to:

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

Module 2: Introduction to Azure Machine Learning

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

Lessons

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

Lab : Introduction to Azure machine learning

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

After completing this module, students will be able to:

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

Module 3: Managing Datasets

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

Lessons

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

Lab : Visualizing Data

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

After completing this module, students will be able to:

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

Module 4: Preparing Data for use with Azure Machine Learning

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

Lessons

• Data pre-processing
• Handling incomplete datasets

Lab : Preparing data for use with Azure machine learning

• Explore some data using Power BI
• Clean the data

After completing this module, students will be able to:

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

Module 5: Using Feature Engineering and Selection

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

Lessons

• Using feature engineering
• Using feature selection

Lab : Using feature engineering and selection

• Merge datasets
• Use PCA to reduce dimensions
• Select some variables and edit metadata

After completing this module, students will be able to:

• Use feature engineering to manipulate data
• Use feature selection

Module 6: Building Azure Machine Learning Models

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

Lessons

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

Lab : Building Azure machine learning models

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

After completing this module, students will be able to:

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

Module 7: Using Classification and Clustering with Azure machine learning models

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

Lessons

• Using classification algorithms
• Clustering techniques
• Selecting algorithms

Lab : Using classification and clustering with Azure machine learning models

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

After completing this module, students will be able to:

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

Module 8: Using R and Python with Azure Machine Learning

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

Lessons

• Using R
• Using Python
• Using Jupyter notebooks
• Supporting R and Python

Lab : Using R and Python with Azure machine learning

• Adding R and Python scripts
• Using Python with Visual Studio IDE
• Add a Jupyter notebook
• Run Jupyter notebook
• Import packages for R/Python
• Data visualization using R/Python
• R programming to work on a time series

After completing this module, students will be able to:

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

Module 9: Initializing and Optimizing Machine Learning Models

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

Lessons

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

Lab : Initializing and optimizing machine learning models

• Using hyper-parameters
• Build an ensemble using stacking
• Evaluate the ensemble

After completing this module, students will be able to:

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

Module 10: Using Azure Machine Learning Models

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

Lessons

• Deploying and publishing models
• Exporting data

Lab : Using Azure machine learning models

• Deploy machine learning models
• Consume a published model
• Export data
• Use exported data in machine learning model

After completing this module, students will be able to:

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

Module 11: Using Cognitive Services

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.

Lessons

• Cognitive services overview
• Processing text
• Processing images
• Creating recommendations

Lab : Using Cognitive Services

• Create and run a text processing application
• Create and run an image processing application
• Create and run a recommendation application

After completing this module, students will be able to:

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

Module 12: Using Machine Learning with HDInsight

This module describes how use HDInsight with Azure machine learning.

Lessons

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

Lab : Machine Learning with HDInsight

• Deploy an HDInsight cluster
• Use the HDInsight cluster
• Display data in Power BI

After completing this module, students will be able to:

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

Module 13: Using R Services with Machine Learning

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.

Lessons

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

Lab : Using R services with machine learning

• Deploy DSVM
• Explore the data science VM
• Configure R server
• Run a sample R server application
• Deploy a SQL server 2016 Azure VM
• Configure SQL Server to allow execution of R scripts
• Execute R scripts inside T-SQL statements
• Use R to visualize data

After completing this module, students will be able to:

• Implement interactive queries
• Perform exploratory data analysis