become an Entity Framework (EF) Core developer
By kansiris
April 12, 2020
//
Learn to become an Entity Framework (EF) Core developer by building cross-platform .NET Core applications. Learn to leverage the hands-on experience over open-source and cross-platform ORM tool.
3 Courses
04:59 HH:MM of learning
2 Study Mode Quizzes
48 Lessons
Become a JavaScript Developer
By kansiris
April 12, 2020
//
Learn modern JavaScript/ES6 from scratch as a new to programming or new to the JavaScript ecosystem. Learn programming fundamentals, object-oriented programming and advanced concepts of JavaScript language. Learn to leverage the benefits of JavaScript and JS ecosystem to build enterprise-grade and cloud-based Desktop applications, Cross-platform Mobile apps and Web applications.
5 Courses
07:02 HH:MM of learning
11 Study Mode Quizzes
91 Lessons
We'
DOTNET Interview Questions and Answers Book
By kansiris
March 28, 2020
//
About the Book
.NET is a software development platform developed by Microsoft. .NET provides tools and libraries that allow developers to develop applications and services much easily, faster and secure by using a convenient way. This book covers the following topics.
- .NET 4.x features details.
- Base Class Library (BCL).
- CLR and its components.
- CTS and CLS with their relationships.
- MSIL and JIT compiler.
- Managed and Unmanaged Code.
- Stack and Heap memory management.
- Assembly and GAC.
- Garbage Collector and its methods.
- Garbage Collector algorithm.
SQL Server Interview Questions & Answers Book
By kansiris
March 28, 2020
//
About the Book
Microsoft SQL Server Interview Questions & Answers eBook is for everyone who is aspiring to clear an interview for a SQL based profile job. This eBook is the collection of most frequently asked interview questions and provides short and precise answers to the questions. This eBook provides you with a chance to understand the Microsoft SQL Server concept and get ready for the interview in a short span of time.
Microsoft SQL Server Interview Questions & Answers eBook begins with an introduction to DBMS and RDBMS Core concepts along with the simple data retrieval queries and will quickly take you to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. This book covers the following topics
- DBMS and RDBMS Core concepts
- Various T-SQL Queries asked in interviews
- Database Constraints like Primary key, foreign key, unique key etc.
- Various Statements including DDL, DML, TCL etc.
- Query multiple tables by using joins, subqueries, table expressions, and set operators
- Use transactions in a concurrent environment
- Get started with programmable objects—from variables and batches to user-defined functions, stored procedures, cursor, triggers etc.
How this eBook will be Helpful?
The author has ensured to provide a book which covers questions starting from basic to the most advanced concepts of SQL. By going through this book, you don’t have to spend time on internet to search for SQL tricky interview questions as author has compiled the latest and typical SQL Interview questions in the book. This book contains wide range of question which will help fresher’s and experienced professional to quickly revise the major SQL concepts before appearing for an interview.
Table of Content
- Introducing SQL Server
- Database Basics
- SQL Queries
- SQL Statements
- Functions, Procedures and Exceptions
- Triggers and Tables
- Cursors and Indexes
React Interview Questions and Answers Book
By kansiris
March 28, 2020
//
About the Book
React Interview Questions and Answers eBook is for those who are an aspiring front end developer and preparing for React interviews. This book is compiled starting with the basic concepts of ReactJS along with the advanced interview questions. This guide contains tricky and theoretical ReactJS interview questions and whether you are a fresher or experienced, this guide will prepare you for the interview that will help you crack the React interview easily.
In this book, you will learn about the fundamentals of React like JSX, Component, Virtual DOM, Routing, Deployment, and many other topics. This book is for those who want to learn React and also for those who are going to appear for the React interview to have a bright future in front-end technologies.
How this eBook will be Helpful
ReactJS is gaining popularity as open-source frontend JavaScript and according to the recent research, ReactJS has a market share of about 7.5%. So there is an ample opportunity for ReactJS developer in many reputed companies across the world. React Interview Questions and Answers eBook is the perfect guide to revise all the concepts and major elements of React Js enabling you to clear the interview for ReactJS developer position. You don’t have to spend time on the internet searching for ReactJS tricky interview questions as the author has compiled the most important and latest ReactJS Interview questions in the book.
Table of Content
- Introducing React
- JSX in Depth
- Components
- State
- Props
- Forms
- Event Handling
- Conditional Rendering
- Routing
- React Hooks
Statistics and Machine Learning Online Training
By kansiris
March 27, 2020
//
The statistic for Machine Learning is used for searching in the Web. It is very much helpful for placing Ads, Credit scoring. It can be used for stock trading. It is a very vast concept and can be used for many other applications. Learning statistic for Machine learning is helpful in learning algorithms. Machine learning gives detail idea about IN and OUT, Implementing different algorithms in the application of machine learning. You’ll learn the theoretical and practical concepts of Machine learning. It uses computer algorithms to search for any data. Enroll today and attend Statistics and Machine Learning Online Training free demo by real time expert.
Course Objectives
What are the Course Objectives?
After this Statistics and Machine Learning Online Training Course you will able to understand
Complete knowledge of Statistic for Machine learning.
Principal on Statistic.
Get knowledge of Algorithms.
Learn Mathematical and Heuristic concept of Machine learning.
Learn reinforcement and dimensionality reduction.
Who should go for this Course?
Any IT experienced Professional who want to be Machine Learning developer can join Statistics and Machine Learning Online Training.
Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.
Pre-requisites:
Statistic
Development Concept
Course Curriculum
Statistics
What is Statistics
Descriptive Statistics
Central Tendency Measures
The Story of Average
Dispersion Measures
Data Distributions
Central Limit Theorem
What is Sampling
Why Sampling
Sampling Methods
Inferential Statistics
What is Hypothesis testing
Confidence Level
Degrees of freedom
what is pValue
Chi-Square test
What is ANOVA
Correlation vs Regression
Uses of Correlation & Regression
Machine Learning
Introduction
ML Fundamentals
ML Common Use Cases
Understanding Supervised and Unsupervised Learning Techniques
Clustering
Similarity Metrics
Distance Measure Types: Euclidean, Cosine Measures
Creating predictive models
Understanding K-Means Clustering
Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
Case study
Implementing Association rule mining
Similarity Metrics
What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Recommendation Use-case
Case study
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
How to build Decision trees
What is Classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Decision Tree
Confusion Matrix
Case study
Random Forest Classifier
What is Random Forests
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Case study
Naive Bayes Classifier
Case study
Project Discussion
Problem Statement and Analysis
Various approaches to solve a Data Science Problem
Pros and Cons of different approaches and algorithms.
Linear Regression
Case study
Introduction to Predictive Modeling
Linear Regression Overview
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Case study
Logistic Regression Overview
Data Partitioning
Univariate Analysis
Bivariate Analysis
Multicollinearity Analysis
Model Building
Model Validation
Model Performance Assessment
Scorecard
Text Mining
Case study
Sentimental Analysis
Case study
Support Vector Machines
Case Study
Introduction to SVMs
SVM History
Vectors Overview
Decision Surfaces
Linear SVMs
The Kernel Trick
Non-Linear SVMs
The Kernel SVM
Deep Learning
Case Study
Deep Learning Overview
The Brain vs Neuron
Introduction to Deep Learning
Introduction to Artificial Neural Networks
The Detailed ANN
The Activation Functions
How do ANNs work & learn
Gradient Descent
Stochastic Gradient Descent
Backpropogation
Convolutional Neural Networks
Convolutional Operation
Relu Layers
What is Pooling vs Flattening
Full Connection
Softmax vs Cross Entropy
What are RNNs – Introduction to RNNs
Recurrent neural networks rnn
LSTMs for beginners – understanding LSTMs
long short term memory neural networks lstm in python
Time Series Analysis
Describe Time Series data
Format your Time Series data
List the different components of Time Series data
Discuss different kind of Time Series scenarios
Choose the model according to the Time series scenario
Implement the model for forecasting
Explain working and implementation of ARIMA model
Illustrate the working and implementation of different ETS models
Forecast the data using the respective model
What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
Implement respective model for forecasting
Visualizing and formatting Time Series data
Plotting decomposed Time Series data plot
Applying ARIMA and ETS model for Time Series forecasting
Forecasting for given Time period
Case Study
Course Objectives
What are the Course Objectives?
After this Statistics and Machine Learning Online Training Course you will able to understand
Complete knowledge of Statistic for Machine learning.
Principal on Statistic.
Get knowledge of Algorithms.
Learn Mathematical and Heuristic concept of Machine learning.
Learn reinforcement and dimensionality reduction.
Who should go for this Course?
Any IT experienced Professional who want to be Machine Learning developer can join Statistics and Machine Learning Online Training.
Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.
Pre-requisites:
Statistic
Development Concept
Course Curriculum
Statistics
What is Statistics
Descriptive Statistics
Central Tendency Measures
The Story of Average
Dispersion Measures
Data Distributions
Central Limit Theorem
What is Sampling
Why Sampling
Sampling Methods
Inferential Statistics
What is Hypothesis testing
Confidence Level
Degrees of freedom
what is pValue
Chi-Square test
What is ANOVA
Correlation vs Regression
Uses of Correlation & Regression
Machine Learning
Introduction
ML Fundamentals
ML Common Use Cases
Understanding Supervised and Unsupervised Learning Techniques
Clustering
Similarity Metrics
Distance Measure Types: Euclidean, Cosine Measures
Creating predictive models
Understanding K-Means Clustering
Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
Case study
Implementing Association rule mining
Similarity Metrics
What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Recommendation Use-case
Case study
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
How to build Decision trees
What is Classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Decision Tree
Confusion Matrix
Case study
Random Forest Classifier
What is Random Forests
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Case study
Naive Bayes Classifier
Case study
Project Discussion
Problem Statement and Analysis
Various approaches to solve a Data Science Problem
Pros and Cons of different approaches and algorithms.
Linear Regression
Case study
Introduction to Predictive Modeling
Linear Regression Overview
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Case study
Logistic Regression Overview
Data Partitioning
Univariate Analysis
Bivariate Analysis
Multicollinearity Analysis
Model Building
Model Validation
Model Performance Assessment
Scorecard
Text Mining
Case study
Sentimental Analysis
Case study
Support Vector Machines
Case Study
Introduction to SVMs
SVM History
Vectors Overview
Decision Surfaces
Linear SVMs
The Kernel Trick
Non-Linear SVMs
The Kernel SVM
Deep Learning
Case Study
Deep Learning Overview
The Brain vs Neuron
Introduction to Deep Learning
Introduction to Artificial Neural Networks
The Detailed ANN
The Activation Functions
How do ANNs work & learn
Gradient Descent
Stochastic Gradient Descent
Backpropogation
Convolutional Neural Networks
Convolutional Operation
Relu Layers
What is Pooling vs Flattening
Full Connection
Softmax vs Cross Entropy
What are RNNs – Introduction to RNNs
Recurrent neural networks rnn
LSTMs for beginners – understanding LSTMs
long short term memory neural networks lstm in python
Time Series Analysis
Describe Time Series data
Format your Time Series data
List the different components of Time Series data
Discuss different kind of Time Series scenarios
Choose the model according to the Time series scenario
Implement the model for forecasting
Explain working and implementation of ARIMA model
Illustrate the working and implementation of different ETS models
Forecast the data using the respective model
What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
Implement respective model for forecasting
Visualizing and formatting Time Series data
Plotting decomposed Time Series data plot
Applying ARIMA and ETS model for Time Series forecasting
Forecasting for given Time period
Case Study
C#.NET Online Training
By kansiris
March 27, 2020
//
C# .Net is created by Microsoft. It is a very popular language. It was developed by Anders Hejlsberg. It is one of the most popular languages which is used in different organizations. It is modern and general purpose language. C#.Net is designed for common language only. It is completely object-oriented. Also a component oriented language. C#.NET Online Training is simple and easy to learn. We can say it is a part of .Net framework. It is mostly based on C and C++ programing languages. Enroll today and attend C#.net course by real-time expert
Course Objectives
What are the C#.NET Online Training Objectives?
Complete knowledge of C#.Net
Able to build C# application
Learn and apply OOAD concept
Able to developed Window-based Application
Learn handling and delegates of events
Creating and handling of multi-threading
Who should go for this C#.NET Online Course?
Any IT experienced Professional who are interested to develop the application in C#.Net programing language.
Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.
Pre-requisites:
.Net programming concepts
C Language
Course Curriculum
INTRODUCTION TO .NET
.Net Framework
Features of .Net
Common Type System
Common Language Specification
Common Language Runtime
MSIL
Base Class Library
Assemblies
Garbage Collection
Stack and Heap
Architecture of GC
Application Domain
.NET FRAMEWORK 3.0 AND 3.5 FEATURES
Silver Light
Multi-Targeting
Multi-Framework Support
LINQ
.NET FRAMEWORK 4.0 FEATURES
Cloud
ASP.NET MVC Web Application
Sharepoint Project
Silverlight Project
.NET FRAMEWORK 4.5 FEATURES
Metro UI Application Develoopment
Gaming Applications
HTML5 and CSS 3 Integration
Semantic Code Analysis
VISUAL C# LANGUAGE
Introduction to VC#
Features of VC#
Data Types
Value Types and Reference Types
Type Conversion
Boxing and UnBoxing
Basic Programming Constructs
Statements and Expressions
Methods, Arrays
Structures and Enumerations
OBJECT ORIENTED PROGRAMMING WITH VC#
Classes and Objects
Interface
Data Encapsulation, Data Abstraction
Fields and Properties
Access Modifiers
Abstract Classes and Sealed Classes
Constructor and Destructor
Static and Instance Members
Inheritance
Method Overloading and Overriding
Operator Overloading
Delegate – Unicast and Multicast
Event Handling
Collection,Dictionaries,String,String builder
Indexers
Attributes, Namespaces,Generics
Anonymous methods, Iterators
Partial Types
Nullable Types
GUI APPLICATION DEVELOPMENT
Windows Forms
Controls, their Properties and Events
Programming with Advanced Controls
CREATING USER DEFINED CONTROLS ERROR HANDLING
Structured Error Handling
Debugging the Application
Dubug and Trace Classes
ADO.NET
Introduction to ADO.NET
ADO.NET Architecture
ADO.NET Managed Providers
Connection and Command Objects
DataReader
DataAdapter and DataSet
DataRelations and DataSet
Connected and Disconnected Environment
Connection Pooling
ADO.NET Exceptions
Using Stored Procedures
N-tier Application
ADO.NET and XML
LINQ
Linq to object
Linq to sql
Linq to XML
ADO.NET Entity Framework
Object Relational Mapping
XML
XML basics
System.Xml Namespace
Classess related to XML
XML Derived Technologies- XSD,XSL,SOAP,WSDL
FILE HANDLING MULTI THREADING
Thread Life Cycle
Thread Synchronization
ASSEMBLIES
Introduction to Assemblies
Disadvantages of COM
Creating Private and Shared Assemblies
Strong name, GAC, COM Interoperability
Satellite Assembly
.NET REMOTING
Distributed Architecture
Remoting and Web Service Comparison
DCOM
Drawbacks of DCOM
Channels, Formatters, Activation
WINDOWS SERVICES
Service Base Class
Service Installer
Service Process installer
Install Utility
CRYSTAL REPORTS
Different Versions of Crystal Reports
Developing a Crystal Report
SETUP AND DEPLOYMENT
Setup and Deployment
WINDOWS COMMUNICATION FOUNDATION
Introduction
Contracts
Security
Using different protocols etc
C# 6.0 NEW FEATURES
Course Objectives
What are the C#.NET Online Training Objectives?
Complete knowledge of C#.Net
Able to build C# application
Learn and apply OOAD concept
Able to developed Window-based Application
Learn handling and delegates of events
Creating and handling of multi-threading
Who should go for this C#.NET Online Course?
Any IT experienced Professional who are interested to develop the application in C#.Net programing language.
Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.
Pre-requisites:
.Net programming concepts
C Language
Course Curriculum
INTRODUCTION TO .NET
.Net Framework
Features of .Net
Common Type System
Common Language Specification
Common Language Runtime
MSIL
Base Class Library
Assemblies
Garbage Collection
Stack and Heap
Architecture of GC
Application Domain
.NET FRAMEWORK 3.0 AND 3.5 FEATURES
Silver Light
Multi-Targeting
Multi-Framework Support
LINQ
.NET FRAMEWORK 4.0 FEATURES
Cloud
ASP.NET MVC Web Application
Sharepoint Project
Silverlight Project
.NET FRAMEWORK 4.5 FEATURES
Metro UI Application Develoopment
Gaming Applications
HTML5 and CSS 3 Integration
Semantic Code Analysis
VISUAL C# LANGUAGE
Introduction to VC#
Features of VC#
Data Types
Value Types and Reference Types
Type Conversion
Boxing and UnBoxing
Basic Programming Constructs
Statements and Expressions
Methods, Arrays
Structures and Enumerations
OBJECT ORIENTED PROGRAMMING WITH VC#
Classes and Objects
Interface
Data Encapsulation, Data Abstraction
Fields and Properties
Access Modifiers
Abstract Classes and Sealed Classes
Constructor and Destructor
Static and Instance Members
Inheritance
Method Overloading and Overriding
Operator Overloading
Delegate – Unicast and Multicast
Event Handling
Collection,Dictionaries,String,String builder
Indexers
Attributes, Namespaces,Generics
Anonymous methods, Iterators
Partial Types
Nullable Types
GUI APPLICATION DEVELOPMENT
Windows Forms
Controls, their Properties and Events
Programming with Advanced Controls
CREATING USER DEFINED CONTROLS ERROR HANDLING
Structured Error Handling
Debugging the Application
Dubug and Trace Classes
ADO.NET
Introduction to ADO.NET
ADO.NET Architecture
ADO.NET Managed Providers
Connection and Command Objects
DataReader
DataAdapter and DataSet
DataRelations and DataSet
Connected and Disconnected Environment
Connection Pooling
ADO.NET Exceptions
Using Stored Procedures
N-tier Application
ADO.NET and XML
LINQ
Linq to object
Linq to sql
Linq to XML
ADO.NET Entity Framework
Object Relational Mapping
XML
XML basics
System.Xml Namespace
Classess related to XML
XML Derived Technologies- XSD,XSL,SOAP,WSDL
FILE HANDLING MULTI THREADING
Thread Life Cycle
Thread Synchronization
ASSEMBLIES
Introduction to Assemblies
Disadvantages of COM
Creating Private and Shared Assemblies
Strong name, GAC, COM Interoperability
Satellite Assembly
.NET REMOTING
Distributed Architecture
Remoting and Web Service Comparison
DCOM
Drawbacks of DCOM
Channels, Formatters, Activation
WINDOWS SERVICES
Service Base Class
Service Installer
Service Process installer
Install Utility
CRYSTAL REPORTS
Different Versions of Crystal Reports
Developing a Crystal Report
SETUP AND DEPLOYMENT
Setup and Deployment
WINDOWS COMMUNICATION FOUNDATION
Introduction
Contracts
Security
Using different protocols etc
C# 6.0 NEW FEATURES