20774 Perform Cloud Data Science with Azure Machine Learning

Inicio:
12-02-2018 / 09:00
Fin:
16-02-2018 / 14:00
Horas:
25
Días:

lunes a viernes

Exámen:

224 €

Imagen_Newsletter:

SharePoint2013logo.png

Precio:
894€

El objetivo principal de este curso es dar a los estudiantes la capacidad de analizar y presentar datos usando Azure Machine Learning, y proporcionar una introducción al uso del aprendizaje automático con herramientas de datos grandes como HDInsight y R Services.

Requisitos previos:

  • Experiencia de programación usando R, y familiaridad con paquetes R comunes
  • Conocimiento de métodos estadísticos comunes y mejores prácticas de análisis de datos.
  • Conocimiento básico del sistema operativo Microsoft Windows y su funcionalidad básica.
  • Conocimiento práctico de bases de datos relacionales.

Dirigido a:

La audiencia principal de este curso es gente que desee analizar y presentar datos utilizando Azure Machine Learning.

El público secundario son profesionales de TI, desarrolladores y trabajadores de la información que necesiten soportar soluciones basadas en el aprendizaje de la máquina Azure.

Objetivos:

Tras finalizar el curso los estudiantes serán capaces de:

  • Explicar el aprendizaje de la máquina y cómo se usan algoritmos e idiomas
  • Describa el propósito de Azure Machine Learning y enumere las principales características de Azure Machine Learning Studio
  • Suba y explore varios tipos de datos a Azure Machine Learning
  • Explorar y usar técnicas para preparar conjuntos de datos listos para usar con Azure Machine Learning
  • Explorar y utilizar técnicas de ingeniería de características y selección de conjuntos de datos que se utilizarán con Azure Machine Learning
  • Explore y utilice algoritmos de regresión y redes neuronales con Azure Machine Learning
  • Explore y use algoritmos de clasificación y agrupación con Azure Machine Learning
  • Utilice R y Python con Azure Machine Learning, y elija cuándo usar un idioma en particular
  • Explorar y utilizar hiperparámetros y múltiples algoritmos y modelos, y ser capaz de anotar y evaluar modelos
  • Explora cómo proporcionar a los usuarios finales los servicios de Aprendizaje de la Máquina Azure y cómo compartir los datos generados con los modelos de Aprendizaje de la Máquina Azure
  • Explorar y utilizar las API de los Cognitive Services para el procesamiento de textos e imágenes, crear una aplicación de recomendación y describir el uso de redes neuronales con Azure Machine Learning
  • Explore y utilice HDInsight con Azure Machine Learning
  • Explore y utilice el servidor R and R con Azure Machine Learning y explique cómo implementar y configurar SQL Server para que admita R services

Temario:

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

After completing this module, students will be able to:

  • Implement interactive queries.
  • Perform exploratory data analysis.

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