Perform Cloud Data Science with Azure Machine Learning

Beskrivelse

På kurset lærer du at analysere og præsentere data ved hjælp af Azure Machine Learning. Du lærer bl.a. om de mest anvendte algoritmer, neurale netværk, klassificering, clustering og programmeringssprogene R og Python sammen med Azure Machine Learning. After completing this course, students will be able to:

• 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

Indhold

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 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 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 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 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 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 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 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 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 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 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 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