During this course you will learn how to analyze and present data by using Azure Machine Learning, and to get an introduction to the use of machine learning with big data tools such as HDInsight and R Services.
The primary audience for this course is people who wish to analyze and present data by using Azure Machine Learning.
The secondary audience is IT professionals, Developers, and information workers who need to support solutions based on 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
Module 2: Introduction to Azure Machine Learning
This module describes the purpose of Azure Machine Learning, and lists 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
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
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
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
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
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
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
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
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
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
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
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
In addition to their professional experience, students who attend this course 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.