Microsoft - MCSA: Machine Learning

Curriculum

Course 20773A: Analysing 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

After completing this module, you’ll be able to:

• Explain the purpose of R server.
• Connect to R server from R client
• Explain the purpose of 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.

Lessons

• Understanding ScaleR data sources
• Reading data into an XDF object
• Summarising 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

After completing this module, you’ll be able to:

• Explain ScaleR data sources
• Describe how to import XDF data
• Describe how to summarise data held in XCF format

Module 3: Visualising Big Data

Explain how to visualize data by using graphs and plots.

Lessons

• Visualising In-memory data
• Visualising big data

Lab : Visualizing data

• Using ggplot to create a faceted plot with overlays
• Using rxlinePlot and rxHistogram

After completing this module, you’ll be able to:

• Use ggplot2 to visualise in-memory data
• Use rxLinePlot and rxHistogram to visualise big data

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

After completing this module, you’ll be able to:

• Transform big data using rxDataStep
• Perform sort and merge operations over big data sets

Module 5: Parallelising 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 parallelise operations

• Using rxExec to maximise resource use
• Creating and using a PEMA class

After completing this module, you’ll be able to:

• Use the rxLocalParallel compute context with rxExec
• Use the RevoPemaR package to write customised scalable and distributable analytics.

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

After completing this module, you’ll be able to:

• Cluster big data to reduce the size of a dataset.
• Create linear and logit regression models and use them to make predictions.

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

After completing this module, you’ll be able to:

• Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
• 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.

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

After completing this module, you’ll be able to:

• Use R in the SQL Server and Hadoop environments.
• Use ScaleR functions with Hadoop on a Map/Reduce cluster to analyse big data.

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
• Run a simple experiment from gallery
• Evaluate an experiment

After completing this module, you’ll be able to:

• Describe machine learning
• Describe machine learning algorithms
• Describe 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.

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

After completing this module, you’ll be able to:

• Describe Azure machine learning.
• Use the Azure machine learning studio.
• Describe the Azure machine learning platforms and environments.

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 : Visualizing Data

• Prepare Azure SQL database
• Import data
• Visualize data
• Summarize data

After completing this module, you’ll be able to:

• Understand the types of data they have.
• Upload data from a number of different sources.
• Explore the data that has been uploaded.

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

After completing this module, you’ll be able to:

• Pre-process data to clean and normalise it.
• Handle 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.

Lessons

• Using feature engineering
• Using feature selection

Lab : Using feature engineering and selection

• Merge datasets
• Use PCA to reduce dimensions
• Select some variables and edit metadata

After completing this module, you’ll be able to:

• Use feature engineering to manipulate data.
• Use feature selection.

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
• Evaluate machine learning models
• Add further regression models
• Create and run a neural-network based application

After completing this module, you’ll be able to:

• Describe machine learning workflows.
• Explain scoring and evaluating models.
• Describe regression algorithms.
• Use a neural-network.

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

After completing this module, you’ll be able to:

• Use classification algorithms.
• Describe clustering techniques.
• Select appropriate 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.

Lessons

• Using R
• Using Python
• Using Jupyter notebooks
• Supporting R and Python

Lab : Using R and Python with Azure machine learning

• Adding R and Python scripts
• Using Python with Visual Studio IDE
• Add a Jupyter notebook
• Run Jupyter notebook
• Import packages for R/Python
• Data visualisation using R/Python
• R programming to work on a time series

After completing this module, you’ll be able to:

• Explain the key features and benefits of R.
• Explain the key features and benefits of Python.
• Use Jupyter notebooks.
• Support R and Python.

Module 9: Initialising and Optimising 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 ensembles

Lab : Initialising and optimising machine learning models

• Using hyper-parameters
• Build an ensemble using stacking
• Evaluate the ensemble

After completing this module, you’ll be able to:

• Use hyper-parameters.
• Use multiple algorithms and models to create ensembles.
• Score and evaluate ensembles.

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
• Exporting data

Lab : Using Azure machine learning models

• Deploy machine learning models
• Consume a published model
• Export data
• Use exported data in machine learning model

After completing this module, you’ll be able to:

• Deploy and publish models.
• Export data to a variety of targets.

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 text
• Processing images
• Creating recommendations

Lab : Using Cognitive Services

• Create and run a text processing application
• Create and run an image processing application
• Create and run a recommendation application

After completing this module, you’ll be able to:

• Describe cognitive services.
• Process text through an application.
• Process images through an 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

• Deploy an HDInsight cluster
• Use the HDInsight cluster
• Display data in Power BI

After completing this module, you’ll be able to:

• Describe the features and benefits of HDInsight.
• Describe the different HDInsight cluster types.
• Use HDInsight with 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.

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
• Explore the data science VM
• Configure R server
• Run a sample R server application
• Deploy a SQL server 2016 Azure VM
• Configure SQL Server to allow execution of R scripts
• Execute R scripts inside T-SQL statements
• Use R to visualise data

After completing this module, you’ll be able to:

• Implement interactive queries.
• Perform exploratory data analysis.