Machine Learning and AI Workshop at Singapore.


Recently I had conducted  Machine Learning and AI workshop at NetAssist Singapore.  Around 15 attended the workshop. Employees from various IT companies and organizations attended the event.

Topics covered at the workshop-

https://uditha.wordpress.com/2017/11/15/big-data-and-machine-learning-workshop-sri-lanka/

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Microsoft Azure Blockchain as a Service – Getting Started Tutorial.


Creating a Blockchain requires an Azure subscription that can support deploying several virtual machines scale sets and managed disks. If necessary, create a free Azure account to begin.
Once a subscription is secured, go to Azure portal. Select ‘+’, Marketplace (‘See all’), and search for ‘Ethereum Consortium Blockchain’.
The Template Deployment will walk you through configuring the first member’s footprint in the network. The deployment flow is divided into five steps: Basics, Operations Management Suite, Deployment regions, Network size and performance, Ethereum settings.

Basics
Under the ‘Basics’ blade, specify values for standard parameters for any deployment, such as subscription, resource group and basic virtual machine properties.

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Operations Management Suite
The Operations Management Suite (OMS) blade allows you to configure an OMS resource for you network. OMS will collect and surface useful metrics and logs from your network, providing the ability to quickly check the network health or debug issues. The “free” offering of OMS will fail gracefully once capacity is reached.

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Deployment regions
Next, under Deployment regions, specify inputs for Number of region(s) to deploy the consortium network and selection of Azure regions based on the number of regions given. User can deploy in maximum of 5 regions.

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Network size and performance
Next, under ‘Network size and performance’ specify inputs for the size of the consortium network, such as number and size of mining nodes and transaction nodes.
A detailed description of each parameter follows:

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Ethereum Settings
Next, under Ethereum settings, specify Ethereum-related configuration settings, like the network ID and Ethereum account password or genesis block.

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Summary
Click through the summary blade to review the inputs specified and to run basic pre-deployment validation.

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Review legal and privacy terms and click ‘Purchase’ to deploy. If the deployment has more than one region, or is for a consortium network, then this template pre-deploys the necessary VPN Gateways to support network connectivity with other members. Deployment of the gateway can take up to 45 to 50 minutes.

Administrator page
Once the deployment has completed successfully and all resources have been provisioned, you can go to the administrator page to get a simple view of your blockchain network and sanity check the deployment state. The URL of the admin page is the DNS name of the load balancer; it is present in the output section of the template deployment named as ADMIN_SITE.

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The node status is refreshed every 10 seconds. Reload the page via the browser or "Reload" button to update the view.

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Next tutorial will discuss how to Crete wallet and smart contract using Solidity.

Big Data and Machine Learning March Workshop Sri Lanka.


Few days ago I had conducted the workshop on Big Data and Machine Learning. Around 10 attended the workshop. Employees from APIIT, LB Finance, MAS and Sri Lanka telecom came to the event.

Topic covered at the workshop-

https://uditha.wordpress.com/2017/11/15/big-data-and-machine-learning-workshop-sri-lanka/

Next workshop will be held on June 2018.

Microsoft Azure Summit 2018, Jakarta – Event Update.


 

Last week I had joined Microsoft Azure Summit 2018 Jakarta , Indonesia. This event was targeted toward Microsoft digital transformation technologies. Azure and AI technologies such as Azure Machine learning and CNTK highlighted at the AI track.

Lot of companies in Indonesia looking forward for digital transformation with Microsoft technology stack. Some of companies already build solutions around azure and AI technologies.

One session was conducted by Toyota motors Indonesia. They highlighted how they integrated azure machine learning for demand forecasting.
And they are going to implement more solutions with AI.

Around 600 attended this conference. It was held at Balai Kartini Jakarta , Indonesia.

Event Details-

https://uditha.wordpress.com/2018/03/11/microsoft-azure-summit-2018-jakarta/

Big Data and Machine Learning Workshop Sri Lanka–Introduction.


Introduction about the Big Data and Machine Learning workshop and training.

Recorded at Microsoft Singapore.

 

Azure Machine Learning Movie Recommendation Tutorial.


azure machine learning 

This Video tutorial shows you how to get started with Movie Recommendation algorithm using Azure Machine Learning.

 

MCSA: Machine Learning Certifications Sri Lanka.


Machine Learning  exam

Earning an MCSA: Machine Learning demonstrates knowledge relevant to Machine Learning, Data Scientists and Data Analysts positions, particularly those who process and analyze large data sets using R and use Azure cloud services to build and deploy intelligent solutions. It is the first step on your path to becoming a Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).

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

Course Outline

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

Microsoft EXAM – 70-774

Perform Cloud Data Science with Azure Machine Learning

https://www.microsoft.com/en-us/learning/exam-70-774.aspx

 

Second Course 20773A:
Analyzing Big Data with Microsoft R

https://www.microsoft.com/en-us/learning/course.aspx?cid=20773

Microsoft EXAM – 70-773

Analyzing Big Data with Microsoft R

https://www.microsoft.com/en-us/learning/exam-70-773.aspx

 

 

By-
Uditha Bandara is specializes in Microsoft AI Development technologies.  He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).  He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India. He has published several books,articles, tutorials, and demos on his Blog – https://uditha.wordpress.com

· Contact    +94 071-6092918

· udithamail@yahoo.com

· udithait@gmail.com