Koulutukset

Ota yhteyttä

Myyntipalvelu

Myynti

010 4321 001 Ota yhteyttä

Kesto: 3 päivä
Veroton hinta (+ alv 24 %): 1 990 €

Tästä asiakaskohtainen toteutus?

Toteutamme koulutuksia myös asiakaskohtaisina. Jätä yhteystietosi, ja tehdään juuri teille sopiva toteutus.

Voit maksaa:
Koulutuskortti SA-VOUCHER

Paikka ja päiväys

Helsinki
28.10 – 30.10
Finnish Finnish
Ilmoittaudu
2.12 – 4.12
Finnish Finnish
Ilmoittaudu

"Koulutusta juuri siellä missä haluat, juuri silloin kuin haluatl"

795 €
Ilmoittaudu

Jaa

Lataa pdf-muodossa

MOC 20774A: Perform Cloud Data Science with Azure Machine Learning

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.

 

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.

 

Esitiedot

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.

 

Module 1: Introduction to Machine LearningThis 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 SelectionThis 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 ModelsThis 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.

 

Course schedule

The training begins at 9.00 and ends around 16.-16.30. Breakfast is served since 8.30.