I had created this site for post Android and IOS app reviews.You can post your app based on the selected category and upload screenshots, videos and URL for Google play or Apple App store.
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
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
This course is designed to provide software quality assurance and testing professionals with the background and tools necessary to organize manual and automated testing efforts for mobile applications. The main objective of this course is to enhance the course participant’s career as a Mobile Test Engineer. This course would be mainly targeted for Android and IOS applications. This course surveys the state of mobile technology, focuses on the software quality challenges it poses, and offers ways to increase the efficiency and effectiveness of mobile testing.
Introduction to Mobile Applications
· What is Mobile Application
· What is Mobile Application Testing
· Mobile Market, Platforms and Ecosystems
· Overview of Main Mobile Platforms
· Android vs. IOS
Overview of Mobile Applications
· Native Mobile Applications
· Hybrid Mobile Applications
· Mobile Web Applications
Challenges in Mobile Application Testing
· Devices
· Networks
· Screen Resolutions
· Environment
· Hardware Compatibility
· Users
· Automation
Types of Mobile Application Testing
· Functional Testing
· GUI Testing
· Interface Testing
· Compatibility Testing
· Performance Testing
· Security Testing
· Content Testing
· Localization Testing
· Usability Testing
· Interrupt Testing
· Installation/Un-Installation Testing
Mobile Testing Terminology and Checklist
· Emulator/Simulator
· Crashlytics
· Freeze
· Wireframes
· Mockup’s/Comps
· Editorials
· Design
Tools necessary For Mobile Application Testing
· ADT Plugins (Android Development Tools)
· Android –SDK (API’s)
· Android SDK Manager
· Emulator
· Android Virtual Device Manager
· Logcat
· Screen Capture
· ADB (Android Debug Bridge)
· Android Screen Monitor
· XCode
· Simulator
· iTunes
· IPhone Configuration Utility
By- Uditha Bandara is specializes in Mobile technologies for Android and IOS. 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 game demos on his Blog – https://uditha.wordpress.com
Developing on AWS helps developers understand how to use the AWS SDK to develop secure and scalable cloud applications. The course provides in-depth knowledge about how to interact with AWS using code and covers key concepts, best practices, and troubleshooting tips.
Course Objectives
Set up the AWS SDK and developer credentials for Java, C#/.Net
Use the AWS SDK to interact with AWS services and develop solutions.
Use Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB as data stores.
Use Web Identity Framework and Amazon Cognito for user authentication.
Use Amazon Mobile development SDK for Android and IOS
Deploy AWS applications
Course Outline
1: AN INTRODUCTION TO AMAZON WEB SERVICES A background of AWS and its needs The AWS Management Console AWS security measures AWS interaction through the SDK and IDE tools
2: WORKING WITH AWS STORAGE SERVICES AWS storage options Working with Amazon EBS Working with AWS AWS Glacier
3: COMPUTING AND NETWORKING SERVICES Amazon Elastic Compute Cloud Best practices Tools Computing and networking tools and libraries
4: MANAGED SERVICES AND THE DATABASES Amazon DynamoDB Amazon RDS Database tools and libraries DynamoDB local
5: DEPLOYMENT AND MANAGEMENT AWS CloudFormation Alarms with Amazon CloudWatch Identity and Access Management Application deployment using AWS Elastic Beanstalk
6: WORKING WITH THE AWS SIMPLE NOTIFICATION SERVICE – SNS Identifying Amazon SNS The service models of Amazon SNS Accessing SNS using the Management Console The sample code and libraries of SNS
7: BUILDING AN APPLICATION USING AWS An overview of an application Tool selection Creating an application
By- Uditha Bandara is specializes in Cloud 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 game demos on his Blog – https://uditha.wordpress.com
Recently I had conducted Big Data and Machine Learning workshop at Bharti Airtel Lanka. Around 15 attended the workshop. Employees from IT , Marketing and Networking departments came to the workshop.
Introduction to R Programming workshop teaches attendees how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.
Overview
· History of R
· Advantages and disadvantages
· Downloading and installing
Introduction
· Using the R console
· Learning about the environment
· Writing and executing scripts
· Object oriented programming
· Installing packages
· Working directory
· Saving your work
Variable types and data structures
· Variables and assignment
· Data types
· Numeric, character, boolean, and factors
· Data structures
· Vectors, matrices, arrays,
· Assigning new values
· Viewing data and summaries
Base graphics system in R
· Scatterplots, histograms, barcharts, box and whiskers, dotplots
· Labels, legends, titles, axes
· Exporting graphics to different formats
General linear regression
· Linear and logistic models
· Regression plots
· Interaction in regression
By- Uditha Bandara is specializes in Microsoft 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 game demos on his Blog – https://uditha.wordpress.com
This introductory course is designed to familiarize testing professionals with the basics of the Coded UI tests in Microsoft Visual Studio. Testers can build, enhance, and run automated test scripts in Visual Studio. In addition to class lecture, you will be asked to complete labs to reinforce critical concepts and tool functionality. The focus is on the practical application of the Visual Studio Coded UI tests to resolve common functional testing challenges. This course focuses on getting started with Coded UI testing with Visual Studio.
Intended Audience
· Testing professionals
· Quality assurance practitioners
· Managers
· Team leaders
Course Outline
Introduction to Automated Functional Testing
· Benefits of Test Automation
· Visual Studio Interface Overview
Creating a Coded UI Test
· Recording Test Steps
· Method Generation
· Importing Action Recordings
Adding Assertions to the Test
· Assertions using UI Locator
· Manually Coding Assertions
Understanding the UI Map
· Accessing the UI Map
· Understanding Control Names and Parent Windows
Test Executing & Evaluating Results
· Running Coded UI Tests
· Evaluating Log Files
Understanding Coded UI Test Structure
· Test File Types
· Understanding File Relationships
Debugging and Troubleshooting
· Evaluation Assertion Statements
· Storing Expected Results
· Debugging Process
Data-driven Testing
· Creating DataSources
· Creating Data-Driven Tests
· Modifying Assertions with Data Driven Tests
By- Uditha Bandara is specializes in Microsoft 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
Bluechip Technologies - Blog 