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
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- 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
