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MCSA: Machine Learning (70-773 & 70-774)

  • dec 12
    onsdag 12/12/2018 - fredag 18/01/2019
    09:00 - 16:00 Hillerød
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    ons 12 dec 09:00 - fre 14 dec 16:00 Hillerød
    20774
    man 14 jan 09:00 - fre 18 jan 16:00 Hillerød
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  • jan 21
    mandag 21/01/2019 - fredag 15/03/2019
    09:00 - 16:00 Hillerød
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    man 21 jan 09:00 - ons 23 jan 16:00 Hillerød
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    man 11 mar 09:00 - fre 15 mar 16:00 Hillerød
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  • jan 21
    mandag 21/01/2019 - fredag 15/03/2019
    09:00 - 16:00 Århus
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    man 21 jan 09:00 - ons 23 jan 16:00 Århus
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    man 11 mar 09:00 - fre 15 mar 16:00 Århus
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  • mar 18
    mandag 18/03/2019 - fredag 10/05/2019
    09:00 - 17:00 Hillerød
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    man 18 mar 09:00 - ons 20 mar 16:00 Hillerød
    20774
    man 06 maj 09:00 - fre 10 maj 16:00 Hillerød
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Kursusinfo

  • Dette kursus varer 8 dage
  • Der medfølger kursusmateriale til dette kursus
  • Dette kursus koster 10 klip på dit klippekort.
  • Fuld forplejning (Morgenmad, frokost, kage, kaffe og sodavand ad libitum)
  • Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her
  • Eksamen er inkluderet i prisen
  • Med i prisen hører Practice Test
  • Du har i alt adgang til din kursus-pc i 3 uger

Varighed

Dette kursus varer 8 dage

Materialer

Der medfølger kursusmateriale til dette kursus

Klip på klippekort

Dette kursus koster 10 klip på dit klippekort.

Forplejning

Fuld forplejning (Morgenmad, frokost, eftermiddagskage samt kaffe og sodavand ad libitum)

Garanti for beståelse

Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her

Eksamen

Alle eksamensforsøg er inkluderet i prisen

Practice Tests

Med i prisen hører Practice Test

Remote adgang

Du har i alt adgang til din kursus-pc i 3 uger

Microsoft Vouchers

10 vouchers + 7.950 kr. for Certificeringspakken.
Læs mere her

Download PDF

Hen PDF om kurset her

Opnåelse af titlen; MCSA: Machine Learning demonstrerer viden, der er relevant for personer med stillinger inden for Machine Learning, Dataforskere og Data Anlytikere. Og især, for personer der behandler og analyserer store datamængder ved hjælp af R og benytter sig af Azure Cloud Services til at opbygge og implementere intelligente løsninger. 

På de 2 kurser kommer du til at arbejde fokuseret med følgende teknologier:

  • Microsoft R Server
  • SQL R Services
  • Azure Machine Learning
  • Bot Framework
  • Cognitive Services

Hos SkillsHouse har vi udviklet og sammensat en spændende kursuspakke, som sikrer dig din MCSA: Machine Learning certificering. Undervisningen foregår via masser af hands-on og best practices og varetages af professionelle konsulenter og undervisere , som derved sikrer dig en bedre indlærring og brug af dine nye færdigheder. 

Kort fortalt:

- Du sparer ca. kr. 10.000,-
- Garanteret beståelse og alle de eksamensforsøg du skal bruge 
- Alt er inkluderet inkl. autoriseret Microsoft kursusmateriale
- Undervisningen foretages af Microsoft Certified Trainer (MCT)
- Fuld forplejning og selvfølgelig alle de colaer og Red Bull du kan drikke 🙂
- Undervisningen er fra 09.00 til 16.00

Microsoft MCSA: Machine Learning certificeringen består af 2 kurser og 2 tilhørende eksamener:

- Analyzing Big Data with Microsoft R (70-773)
- Perform Cloud Data Science with Azure Machine Learning (70-774)

Nedenstående er beskrivelser af de 2 kurser.

Course Outline for: Analyzing Big Data with Microsoft R (70-773)

 

About this course
The main purpose of the course is to give students the ability to use Microsoft R Server to create and run an analysis on a large dataset, and show how to utilize it in Big Data environments, such as a Hadoop or Spark cluster, or a SQL Server database.

Audience profile
The primary audience for this course is people who wish to analyze large datasets within a big data environment.

The secondary audience are developers who need to integrate R analyses into their solutions.

At course completion

After completing this course, students will be able to:

- Explain how Microsoft R Server and Microsoft R Client work
- Use R Client with R Server to explore big data held in different data stores
- Visualize data by using graphs and plots
- Transform and clean big data sets
- Implement options for splitting analysis jobs into parallel tasks 
- Build and evaluate regression models generated from big data 
- Create, score, and deploy partitioning models generated from big data
- Use R in the SQL Server and Hadoop environments 

Module 1: Microsoft R Server and R Client
Explain how Microsoft R Server and Microsoft R Client work.

Lessons 

  • What is Microsoft R server
  • Using Microsoft R client
  • The ScaleR functions 

Lab : Exploring Microsoft R Server and Microsoft R Client 

  • Using R client in VSTR and RStudio
  • Exploring ScaleR functions
  • Connecting to a remote server 

After completing this module, students will be able to: 

  • Explain the purpose of R server.
  • Connect to R server from R client
  • Explain the purpose of the ScaleR functions. 

Module 2: Exploring Big Data
At the end of this module the student will be able to use R Client with R Server to explore big data held in different data stores.

Lessons 

  • Understanding ScaleR data sources
  • Reading data into an XDF object
  • Summarizing data in an XDF object 

Lab : Exploring Big Data 

  • Reading a local CSV file into an XDF file
  • Transforming data on input
  • Reading data from SQL Server into an XDF file
  • Generating summaries over the XDF data 

After completing this module, students will be able to: 

  • Explain ScaleR data sources
  • Describe how to import XDF data
  • Describe how to summarize data held in XCF format 

Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.

Lessons 

  • Visualizing In-memory data
  • Visualizing big data 

Lab : Visualizing data 

  • Using ggplot to create a faceted plot with overlays
  • Using rxlinePlot and rxHistogram 

After completing this module, students will be able to: 

  • Use ggplot2 to visualize in-memory data
  • Use rxLinePlot and rxHistogram to visualize big data 

Module 4: Processing Big Data
Explain how to transform and clean big data sets.

Lessons 

  • Transforming Big Data
  • Managing datasets 

Lab : Processing big data 

  • Transforming big data
  • Sorting and merging big data
  • Connecting to a remote server 

After completing this module, students will be able to: 

  • Transform big data using rxDataStep
  • Perform sort and merge operations over big data sets 

Module 5: Parallelizing Analysis Operations
Explain how to implement options for splitting analysis jobs into parallel tasks.

Lessons 

  • Using the RxLocalParallel compute context with rxExec
  • Using the revoPemaR package 

Lab : Using rxExec and RevoPemaR to parallelize operations 

  • Using rxExec to maximize resource use
  • Creating and using a PEMA class 

After completing this module, students will be able to: 

  • Use the rxLocalParallel compute context with rxExec
  • Use the RevoPemaR package to write customized scalable and distributable analytics. 

Module 6: Creating and Evaluating Regression Models
Explain how to build and evaluate regression models generated from big data

Lessons 

  • Clustering Big Data
  • Generating regression models and making predictions 

Lab : Creating a linear regression model 

  • Creating a cluster
  • Creating a regression model
  • Generate data for making predictions
  • Use the models to make predictions and compare the results 

After completing this module, students will be able to: 

  • Cluster big data to reduce the size of a dataset.
  • Create linear and logit regression models and use them to make predictions. 

Module 7: Creating and Evaluating Partitioning Models
Explain how to create and score partitioning models generated from big data.

Lessons 

  • Creating partitioning models based on decision trees.
  • Test partitioning models by making and comparing predictions 

Lab : Creating and evaluating partitioning models 

  • Splitting the dataset
  • Building models
  • Running predictions and testing the results
  • Comparing results

 After completing this module, students will be able to: 

  • Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
  • Test partitioning models by making and comparing predictions. 

Module 8: Processing Big Data in SQL Server and Hadoop
Explain how to transform and clean big data sets.

Lessons 

  • Using R in SQL Server
  • Using Hadoop Map/Reduce
  • Using Hadoop Spark

 Lab : Processing big data in SQL Server and Hadoop 

  • Creating a model and predicting outcomes in SQL Server
  • Performing an analysis and plotting the results using Hadoop Map/Reduce
  • Integrating a sparklyr script into a ScaleR workflow

 After completing this module, students will be able to: 

  • Use R in the SQL Server and Hadoop environments.
  • Use ScaleR functions with Hadoop on a Map/Reduce cluster to analyze big data.

 

Course outline for: Perform Cloud Data Science with Azure Machine Learning

About this course
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.

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

Kursusbeskrivelse

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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