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Perform Cloud Data Science with Azure Machine Learning - 20774 Course Outline

 (5 days)

Overview
The main purpose of the course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services.

Audience profile
The primary audience for this course is people who wish to analyze and present data by using Azure Machine Learning.
The secondary audience is IT professionals, Developers , and information workers who need to support solutions based on Azure machine learning.

Prerequisites
In addition to their professional experience, students who attend this course should have:
Programming experience using R, and familiarity with common R packages
Knowledge of common statistical methods and data analysis best practices.
Basic knowledge of the Microsoft Windows operating system and its core functionality.
Working knowledge of relational databases.

At course completion
After completing this course, students will be able to:
Explain machine learning, and how algorithms and languages are used
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio
Upload and explore various types of data to Azure Machine Learning
Explore and use techniques to prepare datasets ready for use with Azure Machine Learning
Explore and use feature engineering and selection techniques on datasets that are to be used with Azure Machine Learning
Explore and use regression algorithms and neural networks with Azure Machine Learning
Explore and use classification and clustering algorithms with Azure Machine Learning
Use R and Python with Azure Machine Learning, and choose when to use a particular language
Explore and use hyperparameters and multiple algorithms and models, and be able to score and evaluate models
Explore how to provide end-users with Azure Machine Learning services, and how to share data generated from Azure Machine Learning models
Explore and use the Cognitive Services APIs for text and image processing, to create a recommendation application, and describe the use of neural networks with Azure Machine Learning
Explore and use HDInsight with Azure Machine Learning
Explore and use R and R Server with Azure Machine Learning, and explain how to deploy and configure SQL Server to support R services

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

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

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

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

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

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

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

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

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

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

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

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

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

View outline in Word

A20774

Attend hands-on, instructor-led Perform Cloud Data Science with Azure Machine Learning - 20774 training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

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Fee:  $2795

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