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., you will live, learn at one of our state-of-the-art education centres. Also youÂ´ll learn to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HD Insight and R Services. Using Insoft â€˜s uniqueâ€¯Lecture | Lab | Review technique, your expert instructor will immerse you inâ€¯Microsoft Official Curriculum, combined with practical exercises. After completing this course, you will be able to:
- Explain how Microsoft R Server and Microsoft R Client work
- Use R Client with R Server to explore big data held in different data stores
- Visualize data by using graphs and plots
- Transform and clean big data sets
- Implement options for splitting analysis jobs into parallel tasks
- Build and evaluate regression models generated from big data
- Create, score, and deploy partitioning models generated from big data
- Use R in the SQL Server and Hadoop environments
- 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
At Insoft , we know your time is valuable. Thatâ€™s why we give you the opportunity to gain your Microsoft MCSA: Machine Learning training and certification in just 6 days. We provide the best conditions for you to learn. With us by your side, encouraging and guiding you along the way, you can enjoy 6 intense, focused days of quality learning in a distraction free environment. Your expert instructor will be working with Insoftâ€™s unique accelerated learning methods, which include our exclusive lecture/lab/review methodology with real life cases putting you in the best possible position to learn and retain knowledge. Sitting your Microsoft MCSA: Machine Learning course with Insoft means:
- Youâ€™ll get more hours of training per day, allowing you to get trained and certified faster and more cost-effectively than with any other training provider.
- You will be trained by one of the most expert instructors in the world.
- You can focus purely on learning in our distraction free environment.
- Dedicated onsite support and access to your classroom at all hours.
- The price you pay is all-inclusive and covers all course materials, exam, accommodation, meals and transportation service.
- The Certification Guarantee allows you to train again for free, if you do not pass first time. You only pay for any exams and labs, and accommodation.
Looking for a 2-step option? You can also take your MCSA: Machine Learning course in smaller steps. These 2 certification courses combined will give you the full MCSA: Machine Learning certification:
The correspondent training has been retired.
CourseÂ 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
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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
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? WeÂ´ll help you decide â€“ 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.