• 100 Queen St W, Brampton, ON L6X 1A4, Canada
  • +1-800-961-0337
START DATE END DATE CLASS TIMINGS MODE LOCATION ACTION
11/05/2018 11/09/2018
  • VLT
Live Online
12/03/2018 12/07/2018
  • VLT
Live Online

About the Course 

The primary motivation behind the course is to give understudies the capacity to break down and show information by utilizing Azure Machine Learning, and to give a prologue to the utilization of machine learning with huge information instruments, for example, HDInsight and R Services. 

Audience Profile

The essential group of onlookers for this course is individuals who wish to break down and display information by utilizing Azure Machine Learning. 

The auxiliary gathering of people is IT experts, Developers , and data specialists who need to help arrangements in view of Azure machine learning. 

At Course Completion

Subsequent to finishing  20774: Perform Cloud Data Science with Azure Machine Learning course, understudies will have the capacity to: 

  • Clarify machine learning, and how calculations and dialects are utilized 
  • Portray the reason for Azure Machine Learning, and rundown the fundamental components of Azure Machine Learning Studio 
  • Transfer and investigate different sorts of information to Azure Machine Learning 
  • Investigate and utilize strategies to plan datasets prepared for use with Azure Machine Learning 
  • Investigate and utilize highlight building and choice procedures on datasets that are to be utilized with Azure Machine Learning 
  • Investigate and utilize relapse calculations and neural systems with Azure Machine Learning 
  • Investigate and utilize grouping and bunching calculations with Azure Machine Learning 
  • Utilize R and Python with Azure Machine Learning, and pick when to utilize a specific dialect 
  • Investigate and utilize hyperparameters and different calculations and models, and have the capacity to score and assess models 
  • Investigate how to furnish end-clients with Azure Machine Learning administrations, and how to share information produced from Azure Machine Learning models 
  • Investigate and utilize the Cognitive Services APIs for content and picture preparing, to make a suggestion application, and portray the utilization of neural systems with Azure Machine Learning 
  • Investigate and utilize HDInsight with Azure Machine Learning 
  • Investigate and utilize R and R Server with Azure Machine Learning, and disclose how to convey and arrange SQL Server to help R administrations 

Module 1: Introduction to Machine Learning 

20774: Perform Cloud Data Science with Azure Machine Learning module presents machine learning and talked about how calculations and dialects are utilized. 

Lessons 

  • What is machine realizing? 
  • Prologue to machine learning calculations 
  • Prologue to machine learning dialects 

Lab : Introduction to machine Learning 

  • Agree to accept Azure machine learning studio account 
  • View a basic examination from display 
  • Assess an investigation 

Subsequent to finishing this module, understudies will have the capacity to: 

  • Depict machine learning 
  • Depict machine learning calculations 
  • Depict machine learning dialects 

Module 2: Introduction to Azure Machine Learning 

Depict the reason for Azure Machine Learning, and rundown the primary components of Azure Machine Learning Studio. 

Lessons 

  • Purplish blue machine learning review 
  • Prologue to Azure machine learning studio 
  • Creating and facilitating Azure machine learning applications 

Lab : Introduction to Azure machine learning 

  • Investigate the Azure machine learning studio workspace 
  • Clone and run a straightforward analysis 
  • Clone an analysis, roll out some straightforward improvements, and run the trial 

In the wake of finishing this module, understudies will have the capacity to: 

  • Portray Azure machine learning. 
  • Utilize the Azure machine learning studio. 
  • Portray the Azure machine learning stages and conditions. 

Module 3: Managing Datasets 

Toward the finish of this module the understudy will have the capacity to transfer and investigate different sorts of information in Azure machine learning. 

Lessons 

  • Classifying your information 
  • Bringing in information to Azure machine learning 
  • Investigating and changing information in Azure machine learning 

Lab : Managing Datasets 

  • Get ready Azure SQL database 
  • Import information 
  • Picture information 
  • Condense information 

In the wake of finishing this module, understudies will have the capacity to: 

  • Comprehend the sorts of information they have. 
  • Transfer information from various diverse sources. 
  • Investigate the information that has been transferred. 

Module 4: Preparing Data for use with Azure Machine Learning 

20774: Perform Cloud Data Science with Azure Machine Learning module gives methods to get ready datasets to use with Azure machine learning. 

Lessons 

  • Information pre-handling 
  • Taking care of deficient datasets 

Lab : Preparing information for use with Azure machine learning 

  • Investigate a few information utilizing Power BI 
  • Clean the information 

In the wake of finishing this module, understudies will have the capacity to: 

  • Pre-process information to clean and standardize it. 
  • Handle deficient datasets. 

Module 5: Using Feature Engineering and Selection 

20774: Perform Cloud Data Science with Azure Machine Learning module portrays how to investigate and utilize highlight building and determination methods on datasets that are to be utilized with Azure machine learning. 

Lessons 

  • Utilizing highlight designing 
  • Utilizing highlight choice 

Lab : Using highlight designing and choice 

  • Get ready datasets 
  • Utilize Join to Merge information 

In the wake of finishing this module, understudies will have the capacity to: 

  • Utilize highlight building to control information. 
  • Utilize highlight choice. 

Module 6: Building Azure Machine Learning Models 

20774: Perform Cloud Data Science with Azure Machine Learning module depicts how to utilize relapse calculations and neural systems with Azure machine learning. 

Lessons 

  • Sky blue machine learning work processes 
  • Scoring and assessing models 
  • Utilizing relapse calculations 
  • Utilizing neural systems 

Lab : Building Azure machine learning models 

  • Utilizing Azure machine learning studio modules for relapse 
  • Make and run a neural-arrange based application 
  • In the wake of finishing this module, understudies will have the capacity to: 
  • Depict machine learning work processes. 
  • Clarify scoring and assessing models. 
  • Depict relapse calculations. 
  • Utilize a neural-arrange. 

Module 7: Using Classification and Clustering with Azure machine learning models 

20774: Perform Cloud Data Science with Azure Machine Learning module depicts how to utilize order and grouping calculations with Azure machine learning. 

Lessons 

  • Utilizing characterization calculations 
  • Bunching methods 
  • Choosing calculations 

Lab : Using characterization and grouping with Azure machine learning models 

  • Utilizing Azure machine learning studio modules for characterization. 
  • Add k-implies area to a trial 
  • Include PCA for peculiarity recognition. 
  • Assess the models 
  • Subsequent to finishing this module, understudies will have the capacity to: 
  • Utilize characterization calculations. 
  • Depict grouping methods. 
  • Select fitting calculations. 

Module 8: Using R and Python with Azure Machine Learning 

20774: Perform Cloud Data Science with Azure Machine Learning module depicts how to utilize R and Python with purplish blue machine learning and pick when to utilize a specific dialect. 

Lessons 

  • Utilizing R 
  • Utilizing Python 
  • Consolidating R and Python into Machine Learning tests 

Lab : Using R and Python with Azure machine learning 

  • Investigating information utilizing R 
  • Dissecting information utilizing Python 

In the wake of finishing this module, understudies will have the capacity to: 

  • Clarify the key components and advantages of R. 
  • Clarify the key elements and advantages of Python. 
  • Utilize Jupyter note pads. 
  • Bolster R and Python. 

Module 9: Initializing and Optimizing Machine Learning Models 

20774: Perform Cloud Data Science with Azure Machine Learning module portrays how to utilize hyper-parameters and different calculations and models, and have the capacity to score and assess models. 

Lessons 

  • Utilizing hyper-parameters 
  • Utilizing different calculations and models 
  • Scoring and assessing Models 

Lab : Initializing and streamlining machine learning models 

  • Utilizing hyper-parameters 
  • Subsequent to finishing this module, understudies will have the capacity to: 
  • Utilize hyper-parameters. 
  • Utilize different calculations and models to make gatherings. 
  • Score and assess troupes. 

Module 10: Using Azure Machine Learning Models 

20774: Perform Cloud Data Science with Azure Machine Learning module investigates how to give end clients Azure machine learning administrations, and how to share information produced from Azure machine learning models. 

Lessons 

  • Conveying and distributing models 
  • Expending Experiments 

Lab : Using Azure machine learning models 

  • Convey machine learning models 
  • Expend a distributed model 
  • In the wake of finishing this module, understudies will have the capacity to: 
  • Send and distribute models. 
  • Fare information to an assortment of targets. 

Module 11: Using Cognitive Services 

20774: Perform Cloud Data Science with Azure Machine Learning module presents the psychological administrations APIs for content and picture preparing to make a suggestion application, and depicts the utilization of neural systems with Azure machine learning. 

Lessons 

  • Subjective administrations review 
  • Preparing dialect 
  • Preparing pictures and video 
  • Prescribing items 

Lab : Using Cognitive Services 

  • Fabricate a dialect application 
  • Fabricate a face recognition application 
  • Fabricate a suggestion application 

In the wake of finishing this module, understudies will have the capacity to: 

  • Depict psychological administrations. 
  • Process message through an application. 
  • Process pictures through an application. 
  • Make a suggestion application. 

Module 12: Using Machine Learning with HDInsight 

20774: Perform Cloud Data Science with Azure Machine Learning module portrays how utilize HDInsight with Azure machine learning. 

Lessons 

  • Prologue to HDInsight 
  • HDInsight group sorts 
  • HDInsight and machine learning models 

Lab : Machine Learning with HDInsight 

  • Arrangement a HDInsight group 
  • Utilize the HDInsight group with MapReduce and Spark 
  • In the wake of finishing this module, understudies will have the capacity to: 
  • Portray the components and advantages of HDInsight. 
  • Portray the diverse HDInsight bunch sorts. 
  • Utilize HDInsight with machine learning models. 

Module 13: Using R Services with Machine Learning 

20774: Perform Cloud Data Science with Azure Machine Learning module depicts how to utilize R and R server with Azure machine learning, and disclose how to send and arrange SQL Server and bolster R administrations. 

Lessons 

  • R and R server review 
  • Utilizing R server with machine learning 
  • Utilizing R with SQL Server 

Lab : Using R administrations with machine learning 

  • Convey DSVM 
  • Set up an example SQL Server database and arrange SQL Server and R 
  • Utilize a remote R session 
  • Execute R contents inside T-SQL explanations 

Subsequent to finishing this module, understudies will have the capacity to: 

  • Execute intelligent inquiries. 
  • Perform exploratory information investigation.

Notwithstanding their expert experience, understudies who go to 20774: Perform Cloud Data Science with Azure Machine Learning course ought to have: 

  • Programming knowledge utilizing R, and nature with regular R bundles 
  • Learning of regular measurable techniques and information investigation best practices. 
  • Essential information of the Microsoft Windows working framework and its center usefulness. 
  • Working information of social databases.

Awards