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Aron Mesterbasic
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.
Your course package is designed to provide maximum learning and convenience. This is included in the price of your course:
Your expert instructor will get you ready for the following exams and certifications, which are included in your course package and covered by the Certification guarantee.
You´ll have the perfect starting point for your training with these prerequisites:
- 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.
Using our engaging learning methodology including a variety of tools, we’ll cover the entire curriculum.
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.
- What is machine learning?
- Introduction to machine learning algorithms
- Introduction to 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.
- Azure machine learning overview
- Introduction to Azure machine learning studio
- Developing and hosting Azure machine learning applications
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.
- Categorizing your data
- Importing data to Azure machine learning
- Exploring and transforming data in Azure machine learning
Module 4: Preparing Data for use with Azure Machine Learning
This module provides techniques to prepare datasets for use with Azure machine learning.
- Data pre-processing
- Handling 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.
- Using feature engineering
- Using feature selection
Module 6: Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.
Azure machine learning workflows
Scoring and evaluating models
Using regression algorithms
Using neural networks
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.
- Using classification algorithms
- Clustering techniques
- Selecting 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.
- Using R
- Using Python
- Incorporating R and Python into Machine Learning experiments
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.
- Using hyper-parameters
- Using multiple algorithms and models
- Scoring and evaluating Models
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.
- Deploying and publishing models
- Consuming Experiments
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.
- Cognitive services overview
- Processing language
- Processing images and video
- Recommending products
Module 12: Using Machine Learning with HDInsight
This module describes how use HDInsight with Azure machine learning.
- Introduction to HDInsight
- HDInsight cluster types
- HDInsight and 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.
- R and R server overview
- Using R server with machine learning
- Using R with SQL Server
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.
- What is Microsoft R server
- Using Microsoft R client
- 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.
- Understanding ScaleR data sources
- Reading data into an XDF object
- Summarizing data in an XDF object
Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.
- Visualizing In-memory data
- Visualizing big data
Module 4: Processing Big Data
Explain how to transform and clean big data sets.
- Transforming Big Data
- Managing datasets
Module 5: Parallelizing Analysis Operations
Explain how to implement options for splitting analysis jobs into parallel tasks.
- Using the RxLocalParallel compute context with rxExec
- Using the revoPemaR package
Module 6: Creating and Evaluating Regression Models
Explain how to build and evaluate regression models generated from big data
- Clustering Big Data
- Generating regression models and making predictions
Module 7: Creating and Evaluating Partitioning Models
Explain how to create and score partitioning models generated from big data.
- Creating partitioning models based on decision trees.
- 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.
- Using R in SQL Server
- Using Hadoop Map/Reduce
- Using Hadoop Spark
Das virtuelle Klassenzimmer ist ein Online-Raum, in dem Sie mit Ihrem Dozenten und Ihren Mitschülern in Echtzeit zusammenarbeiten. Alles geschieht live, und Sie können frei interagieren, diskutieren, Fragen stellen und Ihrem Dozenten dabei zusehen, wie er auf einem Whiteboard präsentiert, die Kursunterlagen und Folien bespricht, mit Übungen arbeitet und diese überprüft.
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Mit Readynez können Sie jeden Kurs bequem von zu Hause oder vom Büro aus absolvieren. Readynez bietet Unterstützung und bewährte Verfahren für Ihr Klassenzimmer zu Hause und Sie können das Lernen mit minimalen Auswirkungen auf Ihr tägliches Leben genießen. Außerdem sparen Sie sich die Kosten und die Umweltbelastung durch Reisen.
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