Machine learning and AI Bootcamp training at Colombo , Sri Lanka.

Recently I did Data Science and Machine learning Bootcamp training at Sri Lanka.

This 3 days training was focus on getting started with Data Science technologies. Azure machine learning studio, R studio, Jupyter Notebook , Spyder with Python for data science. It also includes real world usage of machine learning for regression, classification and product recommendations.

Course Outline –

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

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

Managing Datasets

At the end of this module the student will be able to 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

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

· Using regression algorithms

· Using neural networks

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

Introduction to Python

• Python History

• Installing Python

• Installing IDE

Data types

• Numbers

• Sequences

• File

• Tuples

• Dictionaries

Data Science Intro

• Why Python for Data Science

• Popular packages

• Use cases

• Popular Libraries

• Panda

• Numpy

• Matplotlib

• Scikit-learn

Working with data

• Reading & Writing to different data sources

• Cleaning data

• Visualization

• Data Transformation

Introduction to Deep Learning

· Deep learning basics

· Deep learning tools

· Introduction to neural network

· CNN, ANN, RNN neural network

Introduction to Tensor Flow

· Installing Tensor Flow

· Tensor Flow basics

· Basic Neural Networks using tenser flow

· Advanced Neural Networks using tenser flow (CNN, RNN, ANN)

24  software engineers attended 3 Days of training.

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Global Azure Bootcamp 2019 – Colombo, Sri Lanka.

Recently I did Machine Learning session at Global Azure Bootcamp 2019 –Colombo.

Session Topic- Building a Product Recommendation System using Azure Machine Learning

Session Description-

Recommender systems are one of the most successful and widespread application of machine learning technologies in business .This session will focus on building a Product Recommendation System using Azure Machine Learning and deploying as a web service.
Microsoft Azure Machine Learning Studio is a collaborative tool use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools.

Around 70 attended the session.

Event details-

https://gabccolombo.azurewebsites.net/

React Native Mobile Applications Development Workshop , Sri Lanka.

Recently I had conducted  React Native Mobile Application development workshop at NetAssist Colombo.  Around 40 attended the workshop. Employees from various IT companies and organizations attended the event.

Event Details –

https://uditha.wordpress.com/2019/02/08/react-native-mobile-applications-development-workshop/

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Blockchain Application Development (Dapp) Training Course Colombo | Jakarta | Singapore.

Course Overview

Blockchain Application Development prepares you to design and develop end-to-end decentralized applications (Dapps) – which provide anyone with access to the blockchain’s features and services. You will use Truffle IDE, smart contracts, a simple web client and a MetaMask client. You will learn about the architecture of a Dapp: the front-end client interface, backed by the blockchain and smart contracts. The course covers the basic design of a Dapp, Truffle development process and commands (init, develop, test and migrate), testdriven development of Dapp, Dapp application models and emerging standards.

Course Outline

Module 1: Blockchain Basics

Distributed systems

The history of blockchain

Introduction to blockchain

Types of blockchain

Benefits and limitations of blockchain

Module 2: Decentralization

Decentralization using blockchain

Methods of decentralization

Blockchain and full ecosystem decentralization

Smart contract

Decentralized applications

Platforms for decentralization

Module 3: Ethereum basics

Introduction

Ethereum blockchain

Elements of the Ethereum blockchain

Ether

Mining

Clients and wallets

The Ethereum network

Applications developed on Ethereum

Scalability and security issues

Module 4: Smart Contract Programming Basics

Advantages and Drawbacks of Smart Contracts

Smart Contracts with Solidity

The Layout of a Solidity File

Writing simple smart contract

Creating contracts

Using new keyword

Module 5: Ethereum Development

Setting up a development environment

Algorithms & Techniques

Development tools and clients

Introducing solidity

Decentralized Applications

Module 6: Understanding Deployment and Costs

Understand Development and Deployment Cycles

Understanding Solidity Compilation and Deployment

Gas and Gas-Costs

Upgradeability and Data Migration Techniques

Module 7: Hyperledger

Hyperledger as a protocol

Hyperledger Fabric

Module 8: Alternative Blockchains

Platforms

Blockchain Usage

Best Practices

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Mobile +94 0716092918

Singapore-
+65 86738158

Xamarin Mobile Application Development Training at Softlogic Holdings Colombo, Sri Lanka

http://www.softlogic.lk/

Recently I did Xamarin Mobile Application Development training at Softlogic Holdings Colombo.

I covered following topics at the training.

https://uditha.wordpress.com/2017/05/11/developing-cross-platform-apps-with-c-using-xamarin-course/

13 Software developers from IT Department attended 4 Days Xamarin training.

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Mobile +94 0716092918

MCSA: Machine Learning Certifications Sri Lanka.

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

R Programming Workshop Sri Lanka.

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

· Contact    +94 071-6092918

· udithamail@yahoo.com

· udithait@gmail.com