AngularJS for ASP.NET MVC Training.


AngularJS_logo.svg

asp-mvc

 

In this course, students will learn to develop ASP.NET MVC applications using .NET Framework on server side and using Angularjs framework on client side. Using MVC on both server and client side allows separation of responsibilities within code which makes applications easier to maintain and also improves performance.

OBJECTIVES

In this training, attendees will learn how to:

· Define MVC on server and client side

· Create MVC and Web API applications using ASP.NET MVC on server side

· Create single page web applications using the MVC pattern of AngularJS

· Understand the programming model provided by the AngularJS framework

· Define Angular controllers and directives

· Control Angular data bindings

· Implement Responsive Web Applications with AngularJS

 

DURATION

3 Days

 

CHAPTER 1. INTRODUCTION TO ASP.NET MVC

· Review of ASP.NET Web Forms

· Advantages and Disadvantages of Web Forms

· Model-View-Controller Pattern

· ASP.NET MVC

· Advantages and Disadvantages of ASP.NET MVC

· Goals of ASP.NET MVC

 

CHAPTER 2. INTRODUCTION TO ANGULARJS

· What is AngularJS?

· Scope and Goal of AngularJS

· Using AngularJS

· A Very Simple AngularJS Application

· Building Blocks of an AngularJS Application

· Use of Model View Controller (MVC) Pattern

· A Simple MVC Application

 

CHAPTER 3. ANGULARJS EXPRESSIONS

· Operations Supported in Expressions

· AngularJS Expressions vs JavaScript Expressions

· AngularJS Expressions are Safe to Use!

· Examples of ng-src and ng-href Directives

 

CHAPTER 4. WORKING WITH FORMS

· Forms and AngularJS

· Scope and Data Binding

· Role of a Form

· Using Input Text Box

· Using Radio Buttons

· Using Checkbox

· Using Checkbox – Advanced

· Using Select

· Reacting to Model Changes in a Declarative Way

· Example of Using the ng-change Directive

 

CHAPTER 5. Validation

· Introduction to Form Validation

· Validation and Model Binding

· Input Type Validation

· Validation Directives

· A Note About "required"

· Detecting Validation State

· Showing Error Message

· Other Status Variables

· Styling Input Fields

· Styling Other Areas

· Summary

 

CHAPTER 6. AngularJS Responsive Web Apps

· Setting up an MVC 5 Web Application

· Bundling and Minification

· A View for Our Mini-SPA

· Minifying AngularJS Scripts

· Accessing MVC from an Angular Service

· Client-Side Promises

· Passing Form Data to Controllers

· Validating the View Model

· Showing Errors on the Client

· Using Web API 2

 

By-
Uditha Bandara 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


Contact    071-6092918
udithamail@yahoo.com

udithait@gmail.com

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Sri Lanka`s First Blockchain Developer Group.


blockchain sri lanka

I had created this Developer Discussion group to focus on Blockchain Technologies for Sri Lanka.

You can join this group by visiting following URL –

https://www.facebook.com/groups/199055487499631/

About Ethereum’s Blockchain.

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

Microsoft Bot Application Development Tutorial.


microsoft-bot-framework 

This Video tutorial shows you how to get started with Microsoft Bot Application development using Azure.

 

Mobile Application Testing Course Sri Lanka.


mobile testing

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

· Contact    +94 071-6092918

· udithamail@yahoo.com

· udithait@gmail.com

Amazon Web Service training course Sri Lanka.


aws

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

· Contact    +94 071-6092918

· udithamail@yahoo.com

· udithait@gmail.com