Kursinhalt

Course 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
• 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
• 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

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

Course 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

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

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

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

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

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

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

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

Voraussetzungen

Before taking this course you 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.

Do you have what it takes? We´ll help you decide – Call us to discuss your technical background, experience and qualifications to determine how we can help you succeed in this Insoft programme. Just call us and speak to one of our enrolment consultants.