Data Science and Machine Learning Workshop Sri Lanka.


Data Science and Machine Learning Workshop Sri Lanka

Register Now –

https://goo.gl/Y5VkU2

Event Page-

https://www.facebook.com/events/407562449787674/

React Native Mobile Application Development for Android & IOS Training at Singapore.


React Native

Recently I did React Native Mobile Application Development training at Singapore.

I covered following topics at the training.

  • Introduction
  • Back to JavaScript
  • Core components
  • Styling
  • Navigation
  • Lists
  • Cross Platform APIs
  • IOS specific APIs
  • Android specific APIs
  • Persistence
  • Animations
  • Working with HTTP, network requests
  • Data Architecture using Redux
  • Application Deployment

React Native

React Native

React Native

Five Software developers from CPF attended 3 Days React Native training.

CPF

Central Provident Fund, Singapore

https://www.cpf.gov.sg/

 

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Mobile +94 0716092918

React Native Navigation for Android and IOS.


React Native Navigation 

In React Native there are lot of npm libraries for navigation. Some are only for IOS or Android.

In this we build simple navigation using react navigation.

npm

import packages

 

home page

details page

setting up root page

clip_image002clip_image004

More details about the package-

https://reactnavigation.org/

React Native Mobile Application Development Training Course for Android & IOS.


react-native

 

Introduction

In this section, we make a brief introduction to React Native. We set up machines for all the people that haven’t done that yet. By the end, attendees can create a new project, run it and understand tools available.

– Describe React Native: – Philosophy

– Available platforms

– Available APIs

– Available components

– Bridge

– Prerequisites

– Installation review (show installation instructions)

– Create an app with `react-native init` (mention Expo and Create React Native App command)

– Use `react-native run-ios/android` to run the app

– Describe `react-native-packager`

– Demonstrate `Developer Menu`

– Small exercises to get people familiar

 

Back to JavaScript

In this section, we go through latest ES6/ES7 features that were added, like import / export syntax or fat arrow functions. We also cover basics of React (or just refresh them for people already experienced), like component lifecycle and/or JSX. All these are run and live tested on an app set up in the previous section.

– List all `ES6` and `ES7` features (one per slide, w/o going into details)

– Explain how the code is transpiled

– Explain JSX

– React Component lifecycle

– Open discussion about other features

– A couple of exercises to make everyone comfortable with React / JavaScript

 

Core components

In this section, we cover the basic components, like `<View />` & `<Touchable />`.

– Describe all core components and their `props`

– A couple of exercises based on default `init` template:

– Add few buttons and test out handlers (with `bind` preferably)

– Add a bunch of different `<View />`s

 

Styling

In this section, we, describe `StyleSheet` API, how it implements flexbox and how’s that different from CSS.

– Describe StyleSheet, what are available values, how it works

– Demonstrate `Flexbox`

– Mention that there are UI kits, but we are not going to use them as it’s too advanced for now

– Mention that there are cross-platform styling techniques, like styled-components one can use

– A bunch of exercises to get attendees more familiar with the styling, esp.:

– Flexbox and its properties – implement column/grid layout as presented on a slide

– <Text /> number of lines

 

Navigation

In this section, we demonstrate basic concepts of navigation. We also brief attendees into how’s JS navigation different from fully native one. We list available alternatives as well what we will use throughout today.

– List available navigation solutions

– Brief readers into the one that was selected

– Make them aware of the API and how to think of the route hierarchy

– A couple of exercises working on current `init` template, e.g convert app to a stack, so we can push a new route

 

Lists

In this section, we describe lists and why they are so important in React Native. We describe available alternatives and list how’s `<ScrollView />` different than `<FlatList /> (and <SectionList />)` and when to use each.

– List available scroll solutions

– Explain when to use each

– Describe performance optimizations

– Warn about common pitfalls / issues

– As a demo task, one can create a view that has a list of contacts and each of them can be tapped to move to a new screen (with details)

Cross Platform APIs

We will discuss and implement the most used React Native APIs that work cross platform

IOS specific APIs

We will discuss and implement the most used iOS specific React Native APIs

Android specific APIs

We will discuss and implement the most used Android specific React Native APIs

 

Persistence

In this section, we describe how persistence is done with React Native and how it can be achieved using other technologies.

– Demonstrate persistence using AsyncStorage

– Exercise attendees to persist stuff (literal, more advanced JSON)

– Demonstrate available APIs, like `multiSet` and when it’s better to use what – Mention other tools like `realm`

 

Animations

In this section, we will examine different approaches to animating elements within the app. Specifically, we will check `LayoutAnimation` API and the better – `Animated`. We will briefly talk about performance concerns as well.

– Introduce LayoutAnimation

– Challenge attendees with simple animations – Ask if they feel happy with it?

– Introduce `Animated` as a general solution to the problem

– Encourage them to animate few things on screen

– Perf. wise – mention native driver

 

Working with HTTP, network requests, and accessing restful services

 

Here we look at using both the fetch API as well as Axios for fetching and sending data, and using the returned data to update our application UI.

 

Data Architecture

In this section, we cover both MobX and Redux and talk about how and why they are useful in a React Native app

– Introduce Redux

– Set up a basic redux implementation

– Fetching data and updating our redux store

– Discuss other asynchronous libraries such as Saga and Redux Promise Middleware

 

Application Deployment

In this section, we discuss various settings and configurations that the developer must use and understand to deploy the app to both the Google Play store as well as the Apple App store.

 

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Mobile +94 0716092918

Big Data and Machine Learning Workshop Sri Lanka–Introduction.


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

Recorded at Microsoft Singapore.

 

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

Blue Chip Training and Consulting Corporate Video.


 

Version 1.0 of the Blue Chip Training and Consulting Corporate Video