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Perform Cloud Data Science with Azure Machine Learning (20774)

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Detailed Course Outline

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
  • View 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: Managing Datasets
  • 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
  • Prepare datasets
  • Use Join to Merge data

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
  • 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
  • Incorporating R and Python into Machine Learning experiments
Lab: Using R and Python with Azure machine learning
  • Exploring data using R
  • Analyzing data using Python

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 Models
Lab: Initializing and optimizing machine learning models
  • Using hyper-parameters

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
  • Consuming Experiments
Lab: Using Azure machine learning models
  • Deploy machine learning models
  • Consume a published 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 language
  • Processing images and video
  • Recommending products
Lab: Using Cognitive Services
  • Build a language application
  • Build a face detection application
  • Build 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
  • Provision an HDInsight cluster
  • Use the HDInsight cluster with MapReduce and Spark

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

After completing this module, students will be able to:

  • Implement interactive queries.
  • Perform exploratory data analysis.
 

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