Microsoft - MCSA: Machine Learning | Exam 70-773 & 70-774

In dieser intensiven MCSA: Machine Learning Schulung erlangen Sie die Kenntnisse zur Operationalisierung von Microsoft Azure Machine Learning und Big Data mit R Server und SQL R Services. Sie erlernen die Verarbeitung und Analyse großer Datenmengen mit R sowie die Nutzung von Azure Cloud Services zum Aufbau und Einsatz intelligenter Lösungen.

Ein erfahrener Microsoft Certified Trainer (MCT) leitet den Kurs. Sie lernen mithilfe von Firebrands einzigartiger Lecture | Lab | Review Methode, die Ihnen hilft, neue Kenntnisse schneller zu erlangen und zu behalten. Sie entwickeln Praxisfähigkeiten, die Sie in Real-World Szenarien anwenden können, und werden mit Microsoft R Server, SQL R Services, Azure Machine Learning, Cognitive Services und Bot Framework Technologien vertraut.

Sie behandeln eine Reihe an Big Data, Microsoft R und Cloud Data Science Themen, einschließlich:

• Lesen und Verarbeiten von Big Data
• Aufbau von Vorhersagemodellen mit ScaleR
• Entwicklung von Machine Learning Modellen
• Vorbereitung von Daten zur Analyse in Azure Machine Learning
• Operationalisierung und Management von Azure Machine Learning Services

Im MCSA Machine Learning Kurs werden Sie auf die Prüfungen 70-773: Analyzing Big Data with Microsoft R und 70-774: Perform Cloud Data Science with Azure Machine Learning vorbereitet und legen diese vor Ort während des Kurses ab.

Die MCSA Machine Learning Zertifizierung eignet sich vor allem für diejenigen, die ihre Kenntnisse mit R und Azure Machine Learning belegen möchten, insbesondere in Positionen im Bereich Data Science oder als Data Analyst. Die MCSA Zertifizierung dient außerdem als erster Schritt zum Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).

Kurs 20773A: Analyzing Big Data with Microsoft R

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, you’ll 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, you’ll 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, you’ll 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, you’ll 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, you’ll 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, you’ll 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, you’ll 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, you’ll 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.

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

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
• Run a simple experiment from gallery
• Evaluate an experiment

After completing this module, you’ll 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, you’ll 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 : Visualizing Data

• Prepare Azure SQL database
• Import data
• Visualize data
• Summarize data

After completing this module, you’ll 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, you’ll 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

• Merge datasets
• Use PCA to reduce dimensions
• Select some variables and edit metadata

After completing this module, you’ll 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
• Evaluate machine learning models
• Add further regression models
• Create and run a neural-network based application

After completing this module, you’ll 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, you’ll 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
• Using Jupyter notebooks
• Supporting R and Python

Lab : Using R and Python with Azure machine learning

• Adding R and Python scripts
• Using Python with Visual Studio IDE
• Add a Jupyter notebook
• Run Jupyter notebook
• Import packages for R/Python
• Data visualization using R/Python
• R programming to work on a time series

After completing this module, you’ll 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 ensembles

Lab : Initializing and optimizing machine learning models

• Using hyper-parameters
• Build an ensemble using stacking
• Evaluate the ensemble

After completing this module, you’ll 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
• Exporting data

Lab : Using Azure machine learning models

• Deploy machine learning models
• Consume a published model
• Export data
• Use exported data in machine learning model

After completing this module, you’ll 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 text
• Processing images
• Creating recommendations

Lab : Using Cognitive Services

• Create and run a text processing application
• Create and run an image processing application
• Create and run a recommendation application

After completing this module, you’ll 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

• Deploy an HDInsight cluster
• Use the HDInsight cluster
• Display data in Power BI

After completing this module, you’ll 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
• Explore the data science VM
• Configure R server
• Run a sample R server application
• Deploy a SQL server 2016 Azure VM
• Configure SQL Server to allow execution of R scripts
• Execute R scripts inside T-SQL statements
• Use R to visualize data

After completing this module, you’ll be able to:

• Implement interactive queries.
• Perform exploratory data analysis.

Sie legen folgende Prüfungen im Firebrand Trainingscenter ab, abgedeckt durch unsere Leistungsgarantie.

Prüfung 70-773: Analyzing Big Data with Microsoft R

Technologie: Microsoft R Server, SQL R Services

Sprache: Englisch

Getestete Kenntnisse:

• Read and explore big data
• Process big data
• Build predictive models with ScaleR
• Use R Server in different environments

Prüfung 70-774: Perform Cloud Data Science with Azure Machine Learning

Technologie: Azure Machine Learning, Bot Framework, Cognitive Services

Sprache: Englisch

Getestete Kenntnisse:

• Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning
• Develop Machine Learning Models
• Operationalise and Manage Azure Machine Learning Services
• Use Other Services for Machine Learning

Vor der Teilnahme am Kurs werden folgende Vorkenntnisse empfohlen:

• Erfahrung in der Veröffentlichung effektiver APIs für Knowledge Intelligence
• Kenntnisse in den Azure Data Services und Machine Learning
• Vertrautheit mit gängigen Data Science Prozessen - Filtern und Transformieren von Datensätzen, Abschätzung und Bewertung von Modellen
• Erfahrung in der Arbeit mit R - Schreiben und Debuggen von R Funktionen
• Verständnis von Datenstrukturen
• Grundlegende Kenntnisse von Programmierungskonzepten - Control Flow und Scope
• Vertrautheit mit gängigen statistischen Methoden und Best Practices der Datenanalyse
• Tiefergehendes Verständnis von Datenplattformen - Hadoop, SQL Server und grundlegende T-SQL Funktionen

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