MCSA: Machine Learning

Description

This six-day boot camp is intended to give students an in-depth learning about Microsoft Azure Machine Learning and Big Data with R Server and SQL R Services. By taking this course, you will complete the first step to becoming Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).

This program consists of both Analyzing Big Data with Microsoft R (20773) and Perform Cloud Data Science with Azure Machine Learning. Once you have completed this boot camp, in order to become MCSE certified you must the Exam 70-733: Analyzing Big Data with Microsoft R and Exam 70-744 Perform Cloud Data Science with Azure Machine Learning.

Once a student has completed this course, they will be able to:

Deploy HD Insight 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 Strem 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 is 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
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.

What’s included?

  • Authorized Courseware
  • Intensive Hands on Skills Development with an Experienced Subject Matter Expert
  • Hands on practice on real Servers and extended lab support 1.800.482.3172
  • Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
  • Academy Code of Honor: Test Pass Guarantee
  • Optional: Package for Hotel Accommodations, Lunch and Transportation

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor Led (the best training format we offer)
  • Live Online Classroom – Online Instructor Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

Curriculum

Module 1: Read and Explore Big Data

Read Data with R Server
Summarize data
Visualize data
Module 2: Process Big Data

Process data with rxDataStep
Perform Complex transforms that use transform functions
Manage data sets
Process text using RML packages
Module 3: 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
Module 4: 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
Module 5: 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
Module 6: Develop Machine Learning Models

Select an appropriate algorithm or method
Initialize and train appropriate models
Validate models
Module 7: 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 inside T-SQL statements

Enrolled