Sofia  Maggio

Sofia Maggio

1621420020

ML.NET - Machine Learning with .NET Core - Beginner's Guide

This article will get you started with the fundamentals of Machine Learning and how to get started with Machine Learning with .NET Core i.e. ML.NET. We will even learn different concepts of Machine learning with a brief overview.

Table of Contents

Introduction to Machine Learning

Today’s world is full of data and it is increasing day by day. There is a never-ending list of videos to watch, images to view, music to listen to, restaurants to visit, articles to read, stock market data, and we as humans are generating lots of data by the choices we make i.e. videos we watch, the music we listen to, restaurants we visit, etc.

Machine learning is all about computer algorithms that can make some sense out of the data available in the world today. Machine Learning algorithms improve through the experience without any code changes. For computers, this experience is in the form of data. Experience is built by feeding all this data to the algorithm and this data allows the algorithm to learn and build a model. This data which is fed to algorithms for learning is also known as Traning Data. Based on Training data the Machine Learning algorithm builds a model that allows it to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithm tries to identify the patterns in the data provided and based on this pattern tries to build a model. Once the model is built then additional data, which was not part of training data, is fed to the model to understand the efficiency and correctness of the model built.

Machine Learning is used widely in a variety of application such as

  • Email Filtering
  • Fraud Detection
  • Self Driving Cars
  • Image & Voice Recognition (Siri, Alexa, etc)
  • Stock Market Predictions
  • Recommendations from youtube, Netflix, etc

All entities have been collecting data about you and have been using Machine Learning algorithms to understand your choices so that they can make future suggestions to you based on your choices.

Types of Machine Learning

There are many types of Machine Learning algorithms that you can come across as a practitioner. What will be covering here is traditionally recognized 3 major categories i.e. Supervised, Unsupervised & Reinforcement.

Supervised Learning

As the name suggests it means that the activity will be monitored i.e. observe progress and direct the execution based on the observations. So how do we supervise a Machine Learning model we do so by providing the data to the algorithm which is labeled. For e.g., we provide pictures of cats & dogs to an algorithm to read and build a model to identify whether it’s a cat or a dog but here we provide labeled data i.e. we provide pictures with classification i.e. whether it is a picture of a cat or a dog.

Here the data is labeled to tell the Machine Learning algorithm what patterns it should look for in a cat or in a dog. Once the model is formed with labeled training data new picture of cats and dogs can be fed to the model to understand the correctness of the model.

Unsupervised Learning

In unsupervised learning, data is not labeled i.e. model is allowed to discover patterns on its own. Machine Learning algorithm has to on its own look for patterns and based on these patterns group/classify data provided. Unsupervised learning algorithms are more complex as compared to supervised learning algorithms as there is no information in the data provided and so the algorithm is also not aware of the outcome to be provided.

Unsupervised learning can be used to identify patterns in data which is not known to humans as well. Also, it is easier to get unlabeled data as compared to labeled data as labeling data involved extra work.

#programming #.net core #machine learning #ml.net #beginner's guide

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ML.NET - Machine Learning with .NET Core - Beginner's Guide
Einar  Hintz

Einar Hintz

1602560783

jQuery Ajax CRUD in ASP.NET Core MVC with Modal Popup

In this article, we’ll discuss how to use jQuery Ajax for ASP.NET Core MVC CRUD Operations using Bootstrap Modal. With jQuery Ajax, we can make HTTP request to controller action methods without reloading the entire page, like a single page application.

To demonstrate CRUD operations – insert, update, delete and retrieve, the project will be dealing with details of a normal bank transaction. GitHub repository for this demo project : https://bit.ly/33KTJAu.

Sub-topics discussed :

  • Form design for insert and update operation.
  • Display forms in modal popup dialog.
  • Form post using jQuery Ajax.
  • Implement MVC CRUD operations with jQuery Ajax.
  • Loading spinner in .NET Core MVC.
  • Prevent direct access to MVC action method.

Create ASP.NET Core MVC Project

In Visual Studio 2019, Go to File > New > Project (Ctrl + Shift + N).

From new project window, Select Asp.Net Core Web Application_._

Image showing how to create ASP.NET Core Web API project in Visual Studio.

Once you provide the project name and location. Select Web Application(Model-View-Controller) and uncheck HTTPS Configuration. Above steps will create a brand new ASP.NET Core MVC project.

Showing project template selection for .NET Core MVC.

Setup a Database

Let’s create a database for this application using Entity Framework Core. For that we’ve to install corresponding NuGet Packages. Right click on project from solution explorer, select Manage NuGet Packages_,_ From browse tab, install following 3 packages.

Showing list of NuGet Packages for Entity Framework Core

Now let’s define DB model class file – /Models/TransactionModel.cs.

public class TransactionModel
{
    [Key]
    public int TransactionId { get; set; }

    [Column(TypeName ="nvarchar(12)")]
    [DisplayName("Account Number")]
    [Required(ErrorMessage ="This Field is required.")]
    [MaxLength(12,ErrorMessage ="Maximum 12 characters only")]
    public string AccountNumber { get; set; }

    [Column(TypeName ="nvarchar(100)")]
    [DisplayName("Beneficiary Name")]
    [Required(ErrorMessage = "This Field is required.")]
    public string BeneficiaryName { get; set; }

    [Column(TypeName ="nvarchar(100)")]
    [DisplayName("Bank Name")]
    [Required(ErrorMessage = "This Field is required.")]
    public string BankName { get; set; }

    [Column(TypeName ="nvarchar(11)")]
    [DisplayName("SWIFT Code")]
    [Required(ErrorMessage = "This Field is required.")]
    [MaxLength(11)]
    public string SWIFTCode { get; set; }

    [DisplayName("Amount")]
    [Required(ErrorMessage = "This Field is required.")]
    public int Amount { get; set; }

    [DisplayFormat(DataFormatString = "{0:MM/dd/yyyy}")]
    public DateTime Date { get; set; }
}

C#Copy

Here we’ve defined model properties for the transaction with proper validation. Now let’s define  DbContextclass for EF Core.

#asp.net core article #asp.net core #add loading spinner in asp.net core #asp.net core crud without reloading #asp.net core jquery ajax form #asp.net core modal dialog #asp.net core mvc crud using jquery ajax #asp.net core mvc with jquery and ajax #asp.net core popup window #bootstrap modal popup in asp.net core mvc. bootstrap modal popup in asp.net core #delete and viewall in asp.net core #jquery ajax - insert #jquery ajax form post #modal popup dialog in asp.net core #no direct access action method #update #validation in modal popup

Tia  Gottlieb

Tia Gottlieb

1596336480

Beginners Guide to Machine Learning on GCP

Introduction to Machine Learning

  • Machine Learning is a way to use some set of algorithms to derive predictive analytics from data. It is different than Business Intelligence and Data Analytics in a sense that In BI and Data analytics Businesses make decision based on historical data, but In case of Machine Learning , Businesses predict the future based on the historical data. Example, It’s a difference between what happened to the business vs what will happen to the business.Its like making BI much smarter and scalable so that it can predict future rather than just showing the state of the business.
  • **ML is based on Standard algorithms which are used to create use case specific model based on the data **. For example we can build the model to predict delivery time of the food, or we can build the model to predict the Delinquency rate in Finance business , but to build these model algorithm might be similar but the training would be different.Model training requires tones of examples (data).
  • Basically you train your standard algorithm with your Input data. So algorithms are always same but trained models are different based on use cases. Your trained model will be as good as your data.

ML, AI , Deep learning ? What is the difference?

Image for post

ML is type of AI

AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.

Barrier to Entry Has Fallen

AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to

  • Increasing availability in data (big data).
  • Increase in sophistication in algorithm.
  • And availability of hardware and software due to cloud computing.

GCP Machine Learning Spectrum

Image for post

  • For Data scientist and ML experts , TensorFlow on AI platform is more natural choice since they will build their own custom ML models.
  • But for the users who are not experts will potentially use Cloud AutoML or Pre-trained ready to go model.
  • In case of AutoML we can trained our custom model with Google taking care of much of the operational tasks.
  • Pre-trained models are the one which are already trained with tones of data and ready to be used by users to predict on their test data.

Prebuilt ML Models (No ML Expertise Needed)

  • As discuss earlier , GCP has lot of Prebuilt models that are ready to use to solve common ML task . Such as image classification, Sentiment analysis.
  • Most of the businesses are having many unstructured data sources such as e-mail, logs, web pages, ppt, documents, chat, comments etc.( 90% or more as per various studies)
  • Now to process these unstructured data in the form of text, we should use Cloud Natural Language API.
  • Similarly For common ML problems in the form of speech, video, vision we should use respective Prebuilt models.

#ml-guide-on-gcp #ml-for-beginners-on-gcp #beginner-ml-guide-on-gcp #machine-learning #machine-learning-gcp #deep learning

Sofia  Maggio

Sofia Maggio

1621420020

ML.NET - Machine Learning with .NET Core - Beginner's Guide

This article will get you started with the fundamentals of Machine Learning and how to get started with Machine Learning with .NET Core i.e. ML.NET. We will even learn different concepts of Machine learning with a brief overview.

Table of Contents

Introduction to Machine Learning

Today’s world is full of data and it is increasing day by day. There is a never-ending list of videos to watch, images to view, music to listen to, restaurants to visit, articles to read, stock market data, and we as humans are generating lots of data by the choices we make i.e. videos we watch, the music we listen to, restaurants we visit, etc.

Machine learning is all about computer algorithms that can make some sense out of the data available in the world today. Machine Learning algorithms improve through the experience without any code changes. For computers, this experience is in the form of data. Experience is built by feeding all this data to the algorithm and this data allows the algorithm to learn and build a model. This data which is fed to algorithms for learning is also known as Traning Data. Based on Training data the Machine Learning algorithm builds a model that allows it to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithm tries to identify the patterns in the data provided and based on this pattern tries to build a model. Once the model is built then additional data, which was not part of training data, is fed to the model to understand the efficiency and correctness of the model built.

Machine Learning is used widely in a variety of application such as

  • Email Filtering
  • Fraud Detection
  • Self Driving Cars
  • Image & Voice Recognition (Siri, Alexa, etc)
  • Stock Market Predictions
  • Recommendations from youtube, Netflix, etc

All entities have been collecting data about you and have been using Machine Learning algorithms to understand your choices so that they can make future suggestions to you based on your choices.

Types of Machine Learning

There are many types of Machine Learning algorithms that you can come across as a practitioner. What will be covering here is traditionally recognized 3 major categories i.e. Supervised, Unsupervised & Reinforcement.

Supervised Learning

As the name suggests it means that the activity will be monitored i.e. observe progress and direct the execution based on the observations. So how do we supervise a Machine Learning model we do so by providing the data to the algorithm which is labeled. For e.g., we provide pictures of cats & dogs to an algorithm to read and build a model to identify whether it’s a cat or a dog but here we provide labeled data i.e. we provide pictures with classification i.e. whether it is a picture of a cat or a dog.

Here the data is labeled to tell the Machine Learning algorithm what patterns it should look for in a cat or in a dog. Once the model is formed with labeled training data new picture of cats and dogs can be fed to the model to understand the correctness of the model.

Unsupervised Learning

In unsupervised learning, data is not labeled i.e. model is allowed to discover patterns on its own. Machine Learning algorithm has to on its own look for patterns and based on these patterns group/classify data provided. Unsupervised learning algorithms are more complex as compared to supervised learning algorithms as there is no information in the data provided and so the algorithm is also not aware of the outcome to be provided.

Unsupervised learning can be used to identify patterns in data which is not known to humans as well. Also, it is easier to get unlabeled data as compared to labeled data as labeling data involved extra work.

#programming #.net core #machine learning #ml.net #beginner's guide

Nora Joy

1607006620

Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert