• Cloud Computing 

  • Artificial Intelligence & Data Science 

  • Machine Learning and Deep Learning 

  • Natural Language Processing 

  • Cyber Security 

  • BlockChain 

  • Analytical Reasoning 

  • UX Design 

  • Mobile Application Development 

  • Social Media Marketing 

  • Scientific Computing 

  • Game Development 

Now let us go through them one by one. 


Cloud Computing is a term where anything or everything is provided as a service over the Internet. These are broadly divided into three categories: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Cloud computing was inspired by the cloud symbol. 

A cloud service has three distinct characteristics that make it different from traditional web hosting. It is sold on demand, mainly by the minute or the hour, it is flexible as a user can have as much or as little of a service as they need at any given time, and the service is only managed by the provider. 

A cloud may be private or public. A public cloud sells services to anyone who wants its services. Presently, Amazon Web Services is the largest public cloud provider. A private cloud is a network or a data center that supplies hosted services to a handful number of people. Private or public, the main aim of cloud computing is to provide easy, scalable access to computer resources and IT services. 

Cloud Computing Characteristics and Benefits: 

  • Self Service Provisioning:  End users can use compute resources for almost any type of workload on demand. This eliminates the need for IT administrators to provide and manage compute resources. 

  • Elasticity: Corporations can scale up as computing needs increase and scale down again as demands decrease. This eliminates the need for massive investments in the infrastructure. 

  • Pay per use: Compute resources are measured at a granular level, enabling users to pay only for the resources and workloads they use. 

  • Workload resilience: Cloud service providers typically implement most occurring resources to keep storage and to keep important workloads of user’s running often across multiple global regions. 

  • Migration flexibility: Organizations will move certain workloads to or from the cloud or to different cloud platforms as desired for better cost savings or to use new services as they emerge. 

Types of Cloud Computing Services 

Cloud computing has changed over time, it has been divided into three different service categories: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). 

IaaS providers supply a virtual server instance and storage, and APIs that enable users to migrate workloads to a VM. Users have a fixed storage capacity and can start, stop, access and configure the VM and storage as we desire. IaaS providers offer small, medium, large clouds in addition to customized instances, for various workload needs. 

In the PaaS model, cloud suppliers host development tools on their infrastructures. Users access these tools over the internet using web portals or gateway software. PaaS is used for general software development, and many PaaS suppliers host the software after it’s developed. PaaS providers include Salesforce, AWS Elastic Beanstalk and Google App Engine. 

SaaS is a distribution model that delivers software applications across the internet; these applications are called web services. Users will access SaaS applications and services from any location using a computer or mobile device that has internet connection. Most common example of a SaaS application is Microsoft Office 365 for productivity and email services. 



John McCarthy, who named the term in 1956 and defines it as “the science and engineering of making intelligent machines. 

Some other names for the field have been proposed, which are computational intelligence, synthetic intelligence or computational rationality. 

Artificial intelligence is also used to describe a property of machines or programs which is the intelligence that the system demonstrates. Artificial Intelligence research uses tools and information from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, probability, optimization and logic. 

Artificial Intelligence analysis also overlaps with tasks such as robotics, control systems, data mining, logistics, speech recognition, facial recognition and many other tasks. Learning of the machine is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. 

Application of AI 

Gaming: AI plays a major role in strategic games such as chess, poker, tic-tac-toe, etc., where machines have to think of a large number of possible positions.

Natural Language Processing: It has now been made possible to interact with the computer that understands the language spoken by humans.

Expert Systems: There are some special applications that integrate machines, software, and special data to impart reasoning and advising. They provide explanations and recommendations to the users.

Vision Systems: These are the systems that understand, observe, interpret, and comprehend visual input on the computer. For example,oA spying airplane takes photographs, which are used to figure out spatial information or map of the areas. nowadays, Doctors use the clinical expert system to diagnose the patient. police using computer software that is able to recognize the face of a criminal with the stored portrait created by a forensic artist. 

Speech Recognition: Some intelligent systems have the capability of hearing and understanding the language in terms of sentences and their meanings while a human talks to it. It will still recognize your voice if you use different accents, slang words, noise in the background, change in human noise due to cold, etc.

Handwriting Recognition: The handwriting recognition software reads the text which is written on paper by a pen or on the screen by a stylus. It is also able to recognize the shapes of the letters and convert them into editable text.

Intelligent Robots: Robots are made intelligent enough to perform the tasks given by a human. They have sensors located inside them to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have highly efficient processors, multiple sensors, and huge amounts of memory, to exhibit intelligence. They are also capable of learning from their mistakes and can easily adapt to the new environment.



Data science is a blend of data inference, algorithm development, and technology to solve analytically complex problems which arise in day to day situations. The ultimate goal of Data Science is to use this data in creative ways to generate business value. 



Machine Learning is mainly based on algorithms and they are a sequence of instructions used to solve a problem. Algorithms are developed by programmers to instruct computers in new tasks and are the building blocks of the advanced digital world which we are seeing today. Computer algorithms can organize a large amount of data into information and services that are based on certain instructions and rules. 

Instead of programming the computer every step of the way, this approach offers the computer directions that allow it to learn from data without new instructions at each step by the programmer. This means that computers can be used for new, complicated tasks that were not possible to program manually. Tasks like photo recognition applications for the visually impaired or the task of translating pictures into speech. 

The basic process of machine learning is to give the data which is acquired during training to a learning algorithm. The learning algorithm then generates a new set of rules, based on inferences from the data. By using different types of training data, the same learning algorithm can be used to generate different types of models. For e.g. this type of learning algorithm could be used to teach the computer how to translate different languages or predict the stock market. 

Deep learning is a part of a broader family of machine learning methods based on artificial neural networks with representation learning. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, have been applied to fields including computer vision, speech recognition, Natural Language Processing, AR and VR etc. where they have produced results comparable to and in some cases surpassing human expert performance. 

How Machines Learn? 

Supervised learning: The learning algorithm is given a set of data which is labelled and the desired output. For e.g., pictures of dogs labelled “dog” will help the algorithm identify the rules to classify pictures of dogs. 

 Unsupervised learning: The data given to the learning algorithm is not labelled, and the algorithm is asked to search for patterns in the input data. For e.g., the recommendation system of an e-commerce website where the learning algorithm discovers similar items often bought together. 

 Reinforcement learning: The algorithm interacts with a dynamic environment and keeps learning from experience and provides feedback. For e.g., self-driving cars. 

The current growth in Artificial Intelligence and machine learning is tied to developments in three important areas 

 Data availability: We can say that over 3 billion people are online with around 17 billion connected devices or sensors, which generate a large amount of data which when combined with decreasing costs of data storage, can be easily available for use. Machine learning can use this as training data for learning algorithms and hence making new rules to perform increasingly complex tasks. 

   Computing power: Powerful computers and their ability to connect remote processing power through the Internet and make it possible for machine learning techniques to process these huge amounts of data. 

 Algorithmic innovation: These new machine learning techniques, specifically in neural networks which is also known as “deep learning” have inspired new services, but are also making investments and research in other parts of the field. 

Social and Economic Impact: It is predicted that Artificial Intelligence technologies will bring economic changes which will result in an increase in productivity. This includes the use of machines that will be able to perform new tasks, such as self-driving cars, advanced robots or smart assistants to support people in their daily lives. 


Cybersecurity, an evolving information technology security field mainly focuses on the protection of computers and data from unintended and unauthorized users. There is a heavy demand for the cybersecurity analyst jobs in the wings of the army, police, and software companies. In the current era as the information and data is being hacked, the role of the cybersecurity professionals is going to be extremely challenging. 


Block chain, distributed  or digital ledger mainly for recording the details of financial and non financial transactions. It is a combination of cryptography, programming and networking technologies which makes a revolution in the field of information registration and distribution for making a trustable digital relationship. 


NLP is a combination of three Technologies such as machine learning ,Artificial Intelligence and linguistics to talk to machines, as if they were humans .These Technologies empowers the chatbot Google search engine ,Amazon’s Alexa ,Siri of Apple and  Google translator in the business worlde.It harness the unstructured data, acts as personal digital assistant and helps to make an effective analytical decision.  


Computer graphics is the production of images on computers which can be used in any medium. Images that are used in the graphic design are often produced on computers, as well as the still and moving images we see in animations. The real life images seen in electronic games and computer simulations would not have been created or supported without the enhanced capabilities of modern day computer graphics. 

Computer graphics are also important for scientific visualization, a part of computer graphics that uses images and colours to model complex phenomena such as air currents and electric fields, in which objects are drawn on a computer and analyzed in computer programs. Even the windows-based graphical user interface, which is a common means of interacting with a lot of computer programs, is associated with computer graphics. 


User experience (UX) design is the process of creating products that provides us with meaningful and relevant experiences to the users. This involves the design of the entire process of acquiring the product and including aspects of branding, design, usability, and function. 

User Experience Design is usually associated with terms like User Interface Design and Usability, Usability and User Interface Design are important aspects of UX Design. A UX designer is concerned with the entire process of acquiring and integrating a product which includes aspects of branding, design, usability and function. 

UX designers don’t just focus on creating products that are usable; they concentrate on other aspects of the user experience, such as pleasure, efficiency and fun, too. 


Mobile application development is the process by which a mobile application is developed for mobile devices. 

The difference between a good application and a bad application is usually because of the quality of its user experience (UX). A good UX design is what separates successful apps from unsuccessful ones. Nowadays, mobile users expect a lot from an application such as fast loading time, ease of use and delight during an interaction. If you want your application to be successful, you have to consider UX to be not just a small aspect of design, but an essential part of product strategy.

What to work on in Mobile Development? 

Minimize Cognitive Load: Cognitive load here refers to the amount of brain power required to use the application. The human brain has a limited amount of processing power, so you should keep in mind to not provide too much information at once, it might overwhelm the user and make them abandon the task. 

  • Decluttering: Clutter is one of the worst enemies of good design. By cluttering your interface, you overload users with a lot of information: Every added button, image and icon makes the screen more complicated, so make sure to keep it simple. Clutter is terrible on desktop, but it is more worse on mobile .It’s essential to get rid of anything in a mobile design that is not necessary because reducing clutter will improve comprehension. 

  • Offload Tasks: We look for anything in the design that requires user effort, and look for other alternatives. For example, in some cases you can reuse previously entered data instead of asking the user to type again, or use already available information. 

  • Familiar Screens: Familiar screens are those screens which the users see in many apps. Screens such as “Getting started,” “What’s new” and “Search results” have become standards for mobile applications. They do not require additional explanation because these features are known to the users. This allows users to use prior experience to interact with the application, with no learning curve. 

  • Anticipate User’s Needs: We look for steps in the user journey where users might need help. 

  • Avoid Jargon: Clear communication should always be a top most priority in any mobile application. Use what you know about the audience you are targeting to determine whether certain words or phrases are appropriate. 

  • Make the Design Consistent: Consistency is a fundamental principle of design. Consistency eliminates confusion. Maintaining an overall consistent appearance throughout an application is essential.  


Social media marketing is the most powerful way for businesses to reach prospects and customers. The customers are already interacting with brands via social media, and if you do not speak directly to your audience through social media platforms like Facebook, Twitter, Instagram, and Pinterest, you’re missing out the opportunity to promote your product. Good marketing is the main element of social media marketing and can bring success to your business. 

Social media marketing, or SMM, is a type of internet marketing that involves creating and sharing your content on social media in order to achieve the marketing goals set by your corporation. Social media marketing includes activities like posting text and uploading images or videos, and other content that keeps the audience engaged, as well as paid social media advertising. 

Social Media Marketing can help meet a number of goals, such as: 

  • SMM can help in increasing the website traffic 

  • SMM  can help in building conversions 

  • Raising brand awareness of a product 

  • To create a brand identity and positive brand association 

  • Improving communication and interaction with the key audiences 


Scientific Computing is the collection of tools, techniques, and theories which are required to be solved on a computer. 

Most of these tools, techniques, and theories were originally developed in Mathematics and many of them come long before the introduction of electronic computers.  

This set of mathematical theories and techniques is called Numerical Analysis and constitutes a major part of scientific computing. 

Many of the numerical methods that had been developed for the purpose of hand calculation had to be revised and sometimes abandoned. Considerations that were irrelevant or unimportant for hand calculation now became of utmost importance for the efficient and correct use of a large Computer System. 

Many of these considerations such as programming languages, operating systems, management of large quantities of data, correctness of programs all were put under the new discipline of Computer Science, on which scientific computing now heavily depends. Mathematics still continues to play a vital role in scientific computing because it provides the language for the mathematical models that are solved and information about the availability of a model and it provides the theoretical foundation for the numerical strategies and, increasingly, many of the tools from computer science.


Video game development is the field that consists of many aspects involved in creating a video game. Every video game needs a concept, storyline, graphic design and to make the public release of the product. 

Video game development is a very vast field; it is a combination of game production and game design and requires skills from both fields forming the core of a video game developer’s knowledge. 

A video game developer usually holds a big position in the creation of a video game, who guides the project through multiple phases. 

A video game developer is a mixture of a producer and a programmer, they are coordinating administrators with a great vision who also possess the technical skill to overcome and they also contribute to software engineering, editing and other aspects of game design. 

Roles of a Development Team 

  • Game Producer 

  • Game Artist 

  • Programmer 

  • Graphic Designer 

  • Creative Writer 

  • Storyline Editor 

  • Audio Specialist 

  •  Level Designer 






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