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If you are building an application using Node.js, it can get a little overwhelming since there are a variety of databases to choose from and different ways to build APIs. One way to reduce development time and focus on the problem you are trying to solve is to use a Database as a service to store the data. The advantage of this approach is to use a cloud database system without purchasing hardware which can be cost and time-effective.

One such database service is HarperDB Cloud. To build REST APIs rapidly this service allows us to perform all database operations using a single endpoint. It supports a variety of programming languages such as JavaScript, Java, Python, and so on. Some of the features of HarperDB are the following:

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A data scientist extracts manipulate and generate insights from humongous data. To leverage the power of data science, data scientists apply statistics, programming languages, data visualization, databases, etc.

So, when we observe the required skills for a data scientist in any job description, we understand that data science is mainly associated with Python, SQL, and R. The common skills and knowledge expected from a data scientist in the data science industry includes – Probability, Statistics, Calculus, Algebra, Programming, data visualization, machine learning, deep learning, and cloud computing. Also, they expect non-technical skills like business acumen, communication, and intellectual curiosity.

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Artificial intelligence which gives machines the ability to think and behave like humans are gaining traction since the last decade. These features of artificial intelligence are only there because of its ability to predict certain things accurately, these predictions are based upon one certain technology which we know as machine learning (ML). Machine learning as the name suggests is the computer’s ability to learn new things and improve its functionality over time. The main focus of machine learning is on the development of computer programs that are capable of accessing data and using it to learn for themselves. 

To implement machine learning algorithms, two programming languages, R and Python for machine learning are normally used. Generally, selecting features for training data on machine learning in python is a very complex and technical process. But here we will go over some basic techniques and details regarding what is machine learning and how it works. So, let us start by going into detail regarding what ML is, what feature selection is and how can one select feature using python.

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In this ever-growing network of physical devices, Internet of Things (IoT) has become much more realistic, with smart devices that allow less human input and more machine output. Today, it is very much a reality that smart home hubs, wearable devices, connected cars, industrial Internet, smart retail, and many more consider IoT for functionality. 

The desktop apps, servers, and mobile apps use some programming languages that are more or less the same. So it makes us believe that there is no difference in smart objects as they are like minicomputers. IoT comprises of a three-tier architectural environment that consists of generating the data, organizing the data by the local gateways, or hubs and centralized servers that are geographically distant where all the data ends up.

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As programmers, we have come across many blogs. Some of them have helped us to get started with a new technology, some have helped us become more proficient in a certain technology, while a few serve as a helpline when we encounter a certain problem in our code. Since so many blogs are out there, each related to different technologies, it is impossible to rank them according to their content. In this article, we shall discuss the top blogs having content related to general programming languages, UX Design, UI development, testing, and cloud computing.

1. Stack Overflow

Whenever a software professional gets stuck somewhere in their code, no matter what programming language they are using, Stack Overflow is the place where everyone can look for solutions. It is an open forum where you can post your queries or resolve queries posted by others.

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Lizzie is a dynamically compiled scripting language for .NET, allowing you to incorporate dynamically loaded pieces of code into your C# and F# projects. One of its defining traits, is that, first of all, out of the box, it is literally theoretically impossible to execute malicious code, simply since out of the box, it doesn’t contain a single piece of functionality that changes the state of your computer in any way. If you need such functions, which I assume most would, then creating such functions is as easy as marking your method with an attribute, and making sure it has the correct signature. Below is an example.

using System;
using lizzie; class MainClass
{ [Bind(Name = "write")] object Write(Binder<MainClass> binder, Arguments arguments) { Console.WriteLine(arguments.Get(0)); return null; } public static void Main(string[] args) { // Some inline Lizzie code var code = @" var(@foo, function({ +(bar, ""world"")
}, @bar)) write(foo(""Hello "")) "; // Creating a Lizzie lambda object from the above code, and evaluating it var lambda = LambdaCompiler.Compile<MainClass>(new MainClass(), code); lambda(); // Waiting for user input Console.Read(); }
}

Notice how we "bind" a C# method in the above code to our Lizzie lambda object. This makes the C# method available as a "function" internally within our Lizzie code. This trait allows you to easily extend the language, with whatever domain specific extensions you need to solve your particular problem. This makes the language particularly well suited for "Domain Specific Languages."

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