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Использование Azure Machine Learning для облачной обработки данных. Perform Cloud Data Science with Azure Machine Learning

20774A 5 дн. / 40 ак. ч. Точной даты нет, вы можете зарегистрироваться

About this course

The main purpose of the course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services.

Audience profile

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.

Prerequisites

    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.

At course completion

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

Course details

    Course OutlineModule 1: Introduction to Machine LearningThis 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 LearningDescribe 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 DatasetsAt 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 LearningThis 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 SelectionThis 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 ModelsThis 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 modelsThis 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 LearningThis 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 ModelsThis 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 ModelsThis 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 ServicesThis 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 HDInsightThis 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 LearningThis 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

    After completing this module, students will be able to:

        Implement interactive queries.
        Perform exploratory data analysis.



Информация курса

Курс проводится в Киеве, ул. Шота Руставели 39/41, 8-й этаж , офис 803

Тренер курса: Николай Мастило Николай Мастило
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