20774A: Perform Cloud Data Science with Azure Machine Learning Training

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Perform Cloud Data Science with Azure Machine Learning Training offered by Multisoft Systems gives an ability to the learners to analyze and present the data by using the Azure Machine Learning. This training program also throws a light to the overview of using the Machine Learning with the Big Data tools such as R services and HDInsights.

When you complete the Perform Cloud Data Science with Azure Machine Learning Course, you will be able to:

  • Describe the Machine Learning and the ways of using its algorithms and languages.
  • Explain the main purpose of the Azure Machine Learning and its key features.
  • Explore the use the feature engineering and selection methods on the data sets used with the Azure Machine Learning.
  • Find out the best uses of the HDInsight with the Azure Machine Learning
  • Explore and use the R server with the Azure Machine Learning and define how to organize and configure the SQL Server to provide the best support for R Services.
Target Audience
  • Professionals who want to analyze the data by using the Azure Machine Learning can undergo to this course.
  • This course is perfect for the Developers, IT Professionals, and Information Workers who are looking for the Azure Machine Learning based support solutions.
Prerequisites

Candidates who are willing to join this training course should boast the following skills:

  • Strong knowledge of Relational databases
  • Understanding of common statistical techniques and knowledge of the best practices used in Data Analysis.
  • Basic knowledge of the Microsoft Windows Operating System and its main functionalities.
  • Experience of Programming using R and knowledge of the common R packages.

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- Describe the purpose of Azure Machine Learning, and list 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

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