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Perform Cloud Data Science With Azure Machine Learning (M20774)

Key points about this course


Duration: 5 Days*

Exam: Perform Cloud Data Science with Azure Machine Learning

Accredited: Yes

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  • Receive experienced tuition from our expert Microsoft training instructors
  • Our Microsoft training courses are fully accredited by Microsoft

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Course Information

Perform Cloud Data Science with Azure Machine Learning Course Overview | SQL Server Training | M20774

This 5-day course is intended for delegates who want to learn to analyse 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.

This Perform Cloud Data Science with Azure Machine Learning course is fully accredited by Microsoft through the Microsoft Silver Partnership held by The Knowledge Academy.

After completing this course, delegates 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

Perform Cloud Data Science with Azure Machine Learning Course Outline | SQL Server Training | M20774

This course includes the following modules:

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

This module describes the purpose of Azure Machine Learning, and lists 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

By the end of this module, delegates will have learned to upload and explore various types of data in Azure machine learning.

Lessons:

  • Categorising 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
  • Visualise data
  • Summarise 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
  • Analysing data using Python

 

Module 9: Initialising and Optimising 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: Initialising and optimising 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

Who Should Attend this Course?

The primary audience for this course is individuals who wish to analyse 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

Before attending this course, delegates should possess or be able to demonstrate:

  • 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
Please arrive at the venue at 8:45am.
Before attending this course, delegates should possess or be able to demonstrate: 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
The primary audience for this course is individuals who wish to analyse 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.
We are able to provide support via phone & email prior to attending, during and after the course.
Delegate pack consisting of course notes and exercises, Manual, Experienced Instructor, and Refreshments
This course is 5 days
Once your booking has been placed and confirmed, you will receive an email which contains your course location, course overview, pre-course reading material (if required), course agenda and payment receipts

The Knowledge Academy does not provide an examination for this course. Delegates will be given access to:

  • Tuition from one of our expert trainers
  • Certificate of completion
  • Refreshments

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Key points about this course


Duration: 5 Days*

Exam: Perform Cloud Data Science with Azure Machine Learning

Accredited: Yes


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"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"

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