How To Install Node.js On Ubuntu 18.04

May 31, 2021

Node.js has rapidly become an extremely popular part of technology to host dynamic applications on the internet. The cloud has helped such programming languages become a success. Released back in 2009, Node.js is an open-source, cross-platform, back-end JavaScript runtime environment. It’s mainly a server-side scripting language for the front-end language JavaScript, used to produce the dynamic web page content before it’s displayed on the user's web browser. However, it is also very popular as a full-stack and front-end solution.

In this tutorial, we are going to show you how to install Node.js on Ubuntu 18.04. If you are a junior developer and are only starting out with programming and need help with covering the basics of programming, look no further. We are going to cover 3 ways in which you can install Node.js on Ubuntu in this tutorial - using Apt, using a PPA (Personal Package Archive), using nvm. To understand the syntaxes used in this tutorial, you might need some knowledge about the command line tools. With that being said, let’s dive right into the Node.js Ubuntu installation process.

Install Node.js On Ubuntu Using Apt

There exists a version of Node.js in Ubuntu 18.04’s default repositories that can be used to have a consistent coding experience. This means that you don’t need to go for third-party repositories to install a different version for quick experimentation with the language. You can use the apt package manager to obtain the latest version and install Node on Ubuntu. Here’s how:

Step 1: Open Terminal and update the local package manager first. Here’s the syntax:

sudo apt update

Step 2: Install Node.js from the repositories:

sudo apt install nodejs

Step 3: If you are satisfied with the package in the repositories, this is all you need to do to get set up with Node.js. In most cases, however, you will also need to install npm, the Node.js package manager. Type this:

sudo apt install npm

This will allow you to install specific modules and packages for convenience and better usage of Node.js.

Step 4: Finally, to check which version of Node.js you have installed, type in:

nodejs -v

You have successfully completed the installation process of Node.js on Ubuntu. If you wish to install different versions of Node.js, we are going to cover more flexible and robust methods of installations, so read on to know more.

Install Node.js On Ubuntu Using a PPA

You can get a more recent Ubuntu Node install by adding the PPA (Personal Package Archive) maintained by NodeSource. This is going to have more up-to-date versions of Node.js than the ones in-built in the Ubuntu repositories and lets you have the versions Node.js v12.x and Node.js v14.x. This can come in extremely handy for senior developers who need upgraded versions of the libraries in Node.js. Here’s how you Ubuntu install Node.js using a PPA:

Step 1: Install the PPA to access its contents. Use the curl tool to retrieve the installation script for your preferred version from the home directory:

cd ~

curl -sL -o

Step 2: You can access the contents using the nano text editor or any text editor of your choice:


Step 3: Run the script under sudo:

sudo bash

Step 4: Once the PPA is configured and your local package cache is updated automatically, run the setup script from Nodesource. You can now install the Node.js package the same way you did using apt:

sudo apt install nodejs

Step 5: To check which version of Node.js Ubuntu installation is on your distro after following these steps, type:

nodejs -v



This node package you just installed contains binary as well as npm packages, so you don’t need to install npm separately as we saw before. Some npm packages require the installation of the build-essential package. Type in the following command to do that:

sudo apt install build-essential

You have now successfully completed the Node.js Ubuntu install.

Install Node.js On Ubuntu Using Nvm

This method is an alternative to installing Node.js on Ubuntu using a tool called “nvm” which stands for “Node.js Version Manager”. It works with an independent directory within your home directory. One major advantage of using nvm is that you can install multiple self-contained versions of Node.js without affecting the entire system. You can also juggle between the latest and previous versions of Node.js according to your needs. Here’s how you Node.js install Ubuntu using nvm:

Step 1: Download the nvm installation script using curl. Type in the following command on the Terminal:

curl -sL -o

Step 2: Read the script using nano or any other text editor that you wish to use:


Step 3: Run the script with bash:


Step 4: This will install the software in a subdirectory in your home directory. Once you have nvm installed, you can install multiple versions of Node.js. To access the information of the versions of Node.js available, type in the following command:

nvm ls-remote

The following output will be displayed on your screen, which will show the versions of Node.js available:



       v12.18.3   (Latest LTS: Erbium)


























Step 5: Here, the current LTS version is 12.18.3. This may vary depending on when you are reading this tutorial. Install the latest LTS version by typing the following command:

nvm install 12.18.3

Step 6: To see the version you just installed, you need to use an executable called node. This is specific to installations using nvm. Type in the following command to verify the version used by the shell:

node -v



You have now successfully completed the Node.js install in Ubuntu. If you have multiple Node.js versions, you can see what is currently in use by typing:

nvm ls

To default one of the installed versions, type in the following command:

nvm alias default 12.18.3

 This version will automatically be selected on new sessions.

If you have a query about the options available to you with nvm, by type:

nvm help

Final Word

You now know how to install Node.js on Ubuntu in 3 different methods. Your circumstances and needs and the level at which you are programming will determine which method is best for you. The first method mentioned is the easiest and least time-consuming and is for beginners. The last method using nvm provides the most flexibility.

If you are a heavy programmer and juggle with storing and accessing data, you should check out the cloud solutions provided by E2E. We, at E2E Cloud, are trusted by 3000 clients across the world, and our customers believe we provide the best solutions at affordable rates. Don’t believe us? Check for yourself!

Know more about E2E Cloud -

Contact no - 9599620390 Email -

Latest Blogs
This is a decorative image for Project Management for AI-ML-DL Projects
June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:,product%20on%20the%20right%20platform.

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure