HARTMANN software group

MCSA: Machine Learning Boot-Camp Training in Utica

Enroll in or hire us to teach our MCSA: Machine Learning Boot-Camp class in Utica, New York by calling us @303.377.6176. Like all HSG classes, MCSA: Machine Learning Boot-Camp may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, MCSA: Machine Learning Boot-Camp may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  
The MCSA: Machine Learning boot camp is 6 days of an intense deep dive into Microsoft Azure machine learning and Big Data with R Server and SQL R Services... After completing this boot camp, students will be able to: - Deploy HDInsight Clusters. - Authorizing Users to Access Resources. - Loading Data into HDInsight. - Troubleshooting HDInsight. - Implement Batch Solutions. - Design Batch ETL Solutions for Big Data with Spark - Analyze Data with Spark SQL. - Analyze Data with Hive and Phoenix. - Describe Stream Analytics. - Implement Spark Streaming Using the DStream API. - Develop Big Data Real-Time Processing Solutions with Apache Storm. - Build Solutions that use Kafka and HBase. - 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
Course Length: 6 Days
Course Tuition: $3170 (US)

Prerequisites
Audience Profile Candidates for this exam are data scientists or analysts who process and analyze data sets larger than memory using R, or scientists or analysts who use Azure cloud services to build and deploy intelligent solutions. Candidates should have experience with R, familiarity with data structures, familiarity with basic programming concepts (such as control flow and scope), and familiarity with writing and debugging R functions. Candidates have a good understanding of Azure data services and machine learning and are familiar with common data science processes such as filtering and transforming data sets, model estimation, and model evaluation. Candidates for this exam should have experience publishing effective APIs for knowledge intelligence

Course Outline

  
Exams Included:
  1. Exam 70-773 : Analyzing Big Data with Microsoft R
  2. Exam 70-774: Perform Cloud Data Science with Azure Machine Learning

 

Read and explore big data
  • Read data with R Server
  • Summarize data
  • Visualize data
 
Process big data
  • Process data with rxDataStep
  • Perform complex transforms that use transform functions
  • Manage data sets
  • Process text using RML packages
 
Build predictive models with ScaleR
  • Estimate linear models
  • Build and use partitioning models
  • Generate predictions and residuals
  • Evaluate models and tuning parameters
  • Create additional models using RML packages
 
Use R Server in different environments
  • Use different compute contexts to run R Server effectively
  • Optimize tasks by using local compute context
  • Perform in-database analytics by using SQL Server
  • Implement analysis workflows in the Hadoop ecosystem and Spark
  • Deploy predictive models to SQL Server and Azure Machine Learning
 
Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning
  • Import and export data to and from Azure Machine Learning
  • Explore and summarize data
  • Cleanse data for Azure Machine Learning
  • Perform feature engineering
     
Develop Machine Learning Models
  • Select an appropriate algorithm or method
  • Initialize and train appropriate models
  • Validate models
 
Operationalize and Manage Azure Machine Learning Services
  • Deploy models using Azure Machine Learning
  • Manage Azure Machine Learning projects and workspaces
  • Consume Azure Machine Learning models
  • Consume exemplar Cognitive Services APIs
  • Consume Vision APIs to process images, consume Language APIs to process text, consume Knowledge APIs to create recommendations
 
Use Other Services for Machine Learning
  • Build and use neural networks with the Microsoft Cognitive Toolkit
  • Streamline development by using existing resources
  • Perform data sciences at scale by using HDInsights
  • Perform database analytics by using SQL Server R Services on Azure
  • Deploy a SQL Server 2016 Azure VM, configure SQL Server to allow execution of R scripts, execute R scripts inside T-SQL statements

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