Earning an MCSA: Machine Learning demonstrates knowledge relevant to Machine Learning, Data Scientists and Data Analysts positions, particularly those who process and analyze large data sets using R and use Azure cloud services to build and deploy intelligent solutions. It is the first step on your path to becoming a Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).

Course 20774A:
Perform Cloud Data Science with Azure Machine Learning

Course Outline

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
• View a simple experiment from gallery
• Evaluate an experiment

After completing this module, students will 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, students will 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 : Managing Datasets

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

After completing this module, students will 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, students will be able to:

• Pre-process data to clean and normalize 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

• Prepare datasets
• Use Join to Merge data

After completing this module, students will 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
• Create and run a neural-network based application

After completing this module, students will 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, students will 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
• 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

After completing this module, students will 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: 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.Lessons

• Using hyper-parameters
• Using multiple algorithms and models
• Scoring and evaluating Models

Lab : Initializing and optimizing machine learning models

• Using hyper-parameters

After completing this module, students will 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
• Consuming Experiments

Lab : Using Azure machine learning models

• Deploy machine learning models
• Consume a published model

After completing this module, students will 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 language
• Processing images and video
• Recommending products

Lab : Using Cognitive Services

• Build a language application
• Build a face detection application
• Build a recommendation application

After completing this module, students will 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

• Provision an HDInsight cluster
• Use the HDInsight cluster with MapReduce and Spark

After completing this module, students will 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
• Prepare a sample SQL Server database and configure SQL Server and R
• Use a remote R session
• Execute R scripts inside T-SQL statements

Microsoft EXAM – 70-774

Perform Cloud Data Science with Azure Machine Learning

https://www.microsoft.com/en-us/learning/exam-70-774.aspx

Second Course 20773A:
Analyzing Big Data with Microsoft R

https://www.microsoft.com/en-us/learning/course.aspx?cid=20773

Microsoft EXAM – 70-773

Analyzing Big Data with Microsoft R

https://www.microsoft.com/en-us/learning/exam-70-773.aspx