WHAT is MACHINE LEARNING? INTRODUCTION of MACHINE LEARNING

 

machine learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

What is machine learning?

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

Why is machine learning important?

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

What are the different types of machine learning?

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

• Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
• Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.
• Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
• Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

How does supervised machine learning work?

Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

• Binary classification: Dividing data into two categories.
• Multi-class classification: Choosing between more than two types of answers.
• Regression modeling: Predicting continuous values.
• Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.

How does unsupervised machine learning work?

Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:

• Clustering: Splitting the dataset into groups based on similarity.
• Anomaly detection: Identifying unusual data points in a data set.
• Association mining: Identifying sets of items in a data set that frequently occur together.
• Dimensionality reduction: Reducing the number of variables in a data set.

• How does semi-supervised learning work?

  Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:

  • Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.
  • Fraud detection: Identifying cases of fraud when you only have a few positive examples.
  • Labelling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.

  How does reinforcement learning work?

  Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards -- which it receives when it performs an action that is beneficial toward the ultimate goal -- and avoid punishments -- which it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas such as:

  • Robotics: Robots can learn to perform tasks the physical world using this technique.
  • Video gameplay: Reinforcement learning has been used to teach bots to play a number of video games.
  • Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan out how to allocate resources.

  

  Machine learning is like statistics on steroids.

  Who's using machine learning and what's it used for?

  Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook's news feed.

  Facebook uses machine learning to personalize how each member's feed is delivered. If a member frequently stops to read a particular group's posts, the recommendation engine will start to show more of that group's activity earlier in the feed.

  Behind the scenes, the engine is attempting to reinforce known patterns in the member's online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the news feed will adjust accordingly.

  In addition to recommendation engines, other uses for machine learning include the following:

  • Customer relationship management. CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
  • Business intelligence. BI and analytics vendors use machine learning in their
  • software to identify potentially important data points, patterns of data points and anomalies.
  • Human resource information systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.
  • Self-driving cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
  • Virtual assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

  What are the advantages and disadvantages of machine learning?

  Machine learning has seen use cases ranging from predicting customer behavior to forming the operating system for self-driving cars.

  When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. By collecting customer data and correlating it with behaviors over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.

  Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the ride advertisements in searches.

  But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.

  There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.

  How to choose the right machine learning model

  The process of choosing the right machine learning model to solve a problem can be time consuming if not approached strategically.

  Step 1: Align the problem with potential data inputs that should be considered for the solution. This step requires help from data scientists and experts who have a deep understanding of the problem.

  Step 2: Collect data, format it and label the data if necessary. This step is typically led by data scientists, with help from data wranglers.

  Step 3: Chose which algorithm(s) to use and test to see how well they perform. This step is usually carried out by data scientists.

  Step 4: Continue to fine tune outputs until they reach an acceptable level of accuracy. This step is usually carried out by data scientists with feedback from experts who have a deep understanding of the problem.

  Importance of human interpretable machine learning

  Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it's important for the business to explain how every decision was made. This is especially true in industries with heavy compliance burdens such as banking and insurance.

  Complex models can produce accurate predictions, but explaining to a lay person how an output was determined can be difficult. 

  What is the future of machine learning?

  While machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today's most advanced AI applications.

  Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training and application deployment.

  As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify.

  Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.

  

  Deep learning works in very different ways than traditional machine learning.

  How has machine learning evolved?

  1642 - Blaise Pascal invents a mechanical machine that can add, subtract, multiply and divide.

  1679 - Gottfried Wilhelm Leibniz devises the system of binary code.

  1834 - Charles Babbage conceives the idea for a general all-purpose device that could be programmed with punched cards.

  1842 - Ada Lovelace describes a sequence of operations for solving mathematical problems using Charles Babbage's theoretical punch-card machine and becomes the first programmer.

  1847 - George Boole creates Boolean logic, a form of algebra in which all values can be reduced to the binary values of true or false.

  1936 - English logician and cryptanalyst Alan Turing proposes a universal machine that could decipher and execute a set of instructions. His published proof is considered the basis of computer science.

  1952 - Arthur Samuel creates a program to help an IBM computer get better at checkers the more it plays.

  1959 - MADALINE becomes the first artificial neural network applied to a real-world problem: removing echoes from phone lines.

  1985 - Terry Sejnowski's and Charles Rosenberg's artificial neural network taught itself how to correctly pronounce 20,000 words in one week.

  1997 - IBM's Deep Blue beat chess grandmaster Garry Kasparov.

  1999 - A CAD prototype intelligent workstation reviewed 22,000 mammograms and detected cancer 52% more accurately than radiologists did.

  2006 - Computer scientist Geoffrey Hinton invents the term deep learning to describe neural net research.

  2012 - An unsupervised neural network created by Google learned to recognize cats in YouTube videos with 74.8% accuracy.

  2014 - A chatbot passes the Turing Test by convincing 33% of human judges that it was a Ukrainian teen named Eugene Goostman.

  2014 - Google's AlphaGo defeats the human champion in Go, the most difficult board game in the world.

  2016 - LipNet, DeepMind's artificial intelligence system, identifies lip-read words in video with an accuracy of 93.4%.

  2019 - Amazon controls 70% of the market share for virtual assistants in the U.S.

WHAT IS ROBOT AND HOW WORKS ROBOT??

Robot

A robot is a machine designed to execute one or more tasks automatically with speed and precision. There are as many different types of robots as there are tasks for them to perform.

 

A robot is a type of automated machine that can execute specific tasks with little or no human intervention and with speed and precision. The field of robotics, which deals with robot design, engineering and operation, has advanced remarkably in the last 50 years.

IDC identifies robotics as one of six innovation accelerators driving digital transformation. The others include 3D printing, cognitive computing, next-generation security and virtual reality or augmented reality.

What can robots do?

Essentially, there are as many different types of robots as there are tasks for them to perform. Robots can perform some tasks better than humans, but others are best left to people and not machines.

The following are things robots do better than humans:

• Automate manual or repetitive activities in corporate or industrial settings.
• Work in unpredictable or hazardous environments to spot hazards like gas leaks.
• Process and deliver reports for enterprise security.
• Fill out pharmaceutical prescriptions and prep IVs.
• Deliver online orders, room service and even food packets during emergencies.
• Assist during surgeries.
• Robots can also make music, monitor shorelines for dangerous predators, help with search and rescue and even assist with food preparation.

Connected to the cloud

IDC predicted in its 2019 FutureScape report on robotics that of 40% of G2000 manufacturers will digitally connect (at least) around a third of their robots to cloud platforms to improve agility and operational efficiency by 2023. Furthermore, 25% of retailers will deploy robots to free workers from performing repetitive tasks.

Despite their increasing ubiquity, there are several shortcomings to using robots.

They can, for example:

• perform surgeries but not soothe scared patients;
• sense furtive footsteps in a closed-off area, but not take action against gate-crashers;
• lead exercise sessions for the elderly but not ease their loneliness;
• help medical professionals with diagnoses, but not empathize with patients; and
• learn from data, but not correctly respond to unexpected situations.

The increasingly complex capabilities of robots will eventually eliminate some human tasks, but not all. Current robotics technology can automate only 25% of tasks in unpredictable, human-dependent areas like construction and nursing. But robots depend on human programming -- and they (likely) always will.

Types of robots

There are as many different types of robots as there are tasks.

1. Androids

Androids are robots that resemble humans. They are often mobile, moving around on wheels or a track drive. According to the American Society of Mechanical Engineers, these humanoid robots are used in areas such as caregiving and personal assistance, search and rescue, space exploration and research, entertainment and education, public relations and healthcare, and  manufacturing. As use cases and applications explode, the android market is poised to hit $13 billion by 2026.

2. Telechir

A telechir is a complex robot that is remotely controlled by a human operator for a telepresence system. It gives that individual the sense of being on location in a remote, dangerous or alien environment, and enables them to interact with it since the telechir continuously provides sensory feedback.

3. Telepresence robot

A telepresence robot simulates the experience -- and some capabilities -- of being physically present at a location. It combines remote monitoring and control via telemetry sent over radio, wires or optical fibers, and enables remote business consultations, healthcare, home monitoring, childcare and more.

The da Vinci robotic surgery system enables surgeons to control miniaturized surgical instruments mounted on robotic arms with another arm having a magnified 3D camera. The camera provides doctors with a view of the site as they manipulate the instruments through finger-operated master controls.

4. Industrial robot

The IFR (International Federation of Robotics) defines an industrial robot as an "automatically controlled, reprogrammable multipurpose manipulator programmable in three or more axes." Users can adapt these robots to different applications as well. Combining these robots with AI has helped businesses move them beyond simple automation to higher-level and more complex tasks.

In 2019, there were over 390,000 industrial robots installed worldwide, according to the IFR -- with China, Japan and the U.S. leading the way.

In industrial settings, such robots can do the following:

• optimize process performance;
• automate production to increase productivity and efficiency;
• speed up product development;
• enhance safety; and
• lower costs.

5. Swarm robot

Swarm robots (aka insect robots) work in fleets ranging from a few to thousands, all under the supervision of a single controller. These robots are analogous to insect colonies, in that they exhibit simple behaviors individually, but demonstrate behaviors that are more sophisticated with an ability to carry out complex tasks in total.

6. Smart robot

This is the most advanced kind of robot. The smart robot has a built-in AI system that learns from its environment and experiences to build knowledge and enhance capabilities to continuously improve. A smart robot can collaborate with humans and help solve problems in areas like the following:

• agricultural labor shortages;
• food waste;
• study of marine ecosystems;
• product organization in warehouses; and
• clearing of debris from disaster zones.

Baxter, a smart robot from Rethink Robotics

Common characteristics of robots

Not all robots look like HAL 9000 in 2001: A Space Odyssey or BigDog -- a rough-terrain quadruped robot with complex sensors, gyroscopes and hydraulic actuators -- from Boston Dynamics. Some have human-like features (androids), while others are all mechanical limbs (PackBot). Still others look like keychains (Tamagotchi) or whizzing toys (Roomba).

Nonetheless, all robots have some common characteristics, such as the following:

• mechanical construction
• electrical components
• computer programming

As AI and software advance, robots will become smarter, more efficient and will take on more complex challenges.

RPA automates repetitive tasks so human personnel can focus on more higher-value work. Use cases can be simple (automated email responses) or complex (automating thousands of jobs).

RPA is a steppingstone to more advanced intelligent process automation (IPA). IPA adds decision-making capabilities, AI tools and cognitive technologies like natural language processing and machine learning.

RPA leads to more advanced intelligent process automation systems.

Robots and robotics: A short history

The 1921 play, R.U.R., which stands for Rossum's Universal Robots, by Czech writer Karel Capek, first introduced the word "robot." These robots were artificial people rather than machines, and could think for themselves, so they are somewhat similar to modern androids. Isaac Asimov said that Capek contributed the word robot to all languages in which science fiction is written. Asimov introduced the word robotics and his famous Three Laws of Robotics in his story "Runaround."

The first robots, although they weren't called that at the time, actually date back several centuries before the Roaring Twenties. In 1478, Leonardo da Vinci designed a self-propelled car -- still considered influential for robotic designs. While this autonomous system didn't make it past the drawing board, in 2004 a team of Italian scientists replicated its design as a digital model, proving that it works.

The path-breaking work of Asimov and da Vinci set the stage for the developments that followed. In 1950, English computer scientist Alan Turing developed the Turing Test -- originally called The Imitation Game -- laying the foundation for further research into artificial intelligence and robotics.

Developed by Alan Turing, the Turing Test is a method of inquiry to determine whether an AI device (computer, robot, etc.) can think like a human being.

Stanley Kubrick's 2001: A Space Odyssey presented one of the world's earliest AI robots, the HAL 9000. HAL can recognize speech, understand natural language and even win chess games. Now a part of Carnegie Mellon University's Hall of Fame, HAL still inspires scientists to look for ways to duplicate its 1960s-imagined capabilities.

In the 1950s, George C. Devol invented a reprogrammable manipulator -- Unimate. 

Engineer Joseph Engleberger acquired Devol's robot patent and converted his design into the world's first industrial robot. He eventually earned the label: The Father of Robotics.

In 1966, MIT developed one of the earliest AI-based bots, ELIZA, while SRI International later designed Shakey, a self-directed robot, for specialized industrial applications. By the early 70s, scientists had successfully integrated bots into medicine with MYCIN to help identify bacteria and INTERNIST-1 computer-based diagnostic tool. In the 1980s, ALVINN, the robotics tech that powers today's self-driving cars was developed.

The Tramp foresees the robot's rise

"Don't give yourselves to these unnatural men -- machine men with machine minds and machine hearts! You are not machines, you are not cattle, you are men! You have the love of humanity in your hearts!"

-- Sir Charlie Chaplin, The Great Dictator

In 1940, Sir Charlie Chaplin probably had no idea that the inexorable rise of machines was just a few decades away. Over 80 years later, automated robots are now used in manufacturing, industrial supply chains, agriculture, financial services, education and more.

By the 1990s, consumer-facing bots appeared as computer games like Tamagotchi. Post 2000, interest in robots and robotics exploded with the release of SmarterChild, a programmed bot within AOL Instant Messenger that's now considered the forerunner AI to Apple's Siri.

In the early 2000s PackBot, a military robot, and Stanley, a vehicular bot, were both invented. Notably, PackBot played an important role in the aftermath of the 9/11 attacks, as first responders sent the robot into the rubble to search for victims and assess the structural integrity of the debris. PackBot sent back pictures from hard-to-reach places, helping with the rescue effort.

Stan is a robot that autonomously moves vehicles around for logistics purposes.

The PackBot inspired a new era of robotics, accelerating the development of more advanced, autonomous machines that now help in areas such as the following:

• disaster management
• law enforcement
• weather forecasts
• residential hygiene
• military reconnaissance

Later, household robots like Roomba and AI-based robots like Siri and Alexa paved the way for robots in people's everyday lives, furthering their potential.

Today's robots can carry out a number of complex tasks that would have been written off as science fiction even half a century ago. Smart, intelligent robots are now collaborating with humans and helping to solve problems that looked unsolvable in the past.

Java Tutorial | Learn Java Programming

 

Java Tutorial

Our core Java programming tutorial is designed for students and working professionals. Java is an object-oriented, class-based, concurrent, secured and general-purpose computer-programming language. It is a widely used robust technology.

What is Java?

Java is a programming language and a platform. Java is a high level, robust, object-oriented and secure programming language.

Java was developed by Sun Microsystems (which is now the subsidiary of Oracle) in the year 1995. James Gosling is known as the father of Java. Before Java, its name was Oak. Since Oak was already a registered company, so James Gosling and his team changed the name from Oak to Java.

Platform: Any hardware or software environment in which a program runs, is known as a platform. Since Java has a runtime environment (JRE) and API, it is called a platform.

Java Example

Let's have a quick look at Java programming example. A detailed description of Hello Java example is available in next page.

Simple.java 

Application

1. class Simple{  
2.     public static void main(String args[]){  
3.      System.out.println("Hello Java");  
4.     }  
5. } According to Sun, 3 billion devices run Java. There are many devices where Java is currently used. Some of them are as follows:       

1. Desktop Applications such as acrobat reader, media player, antivirus, etc.
2. Web Applications such as irctc.co.in, javatpoint.com, etc.
3. Enterprise Applications such as banking applications.
4. Mobile
5. Embedded System
6. Smart Card
7. Robotics
8. Games, etc.

Types of Java Applications

There are mainly 4 types of applications that can be created using Java programming:

1) Standalone Application

Standalone applications are also known as desktop applications or window-based applications. These are traditional software that we need to install on every machine. Examples of standalone application are Media player, antivirus, etc. AWT and Swing are used in Java for creating standalone applications.

2) Web Application

An application that runs on the server side and creates a dynamic page is called a web application. Currently, Servlet, JSP, Struts, Spring, Hibernate, JSF, etc. technologies are used for creating web applications in Java.

3) Enterprise Application

An application that is distributed in nature, such as banking applications, etc. is called an enterprise application. It has advantages like high-level security, load balancing, and clustering. In Java, EJB is used for creating enterprise applications.

4) Mobile Application

An application which is created for mobile devices is called a mobile application. Currently, Android and Java ME are used for creating mobile applications.

Java Platforms / Editions

There are 4 platforms or editions of Java:

1) Java SE (Java Standard Edition)

It is a Java programming platform. It includes Java programming APIs such as java.lang, java.io, java.net, java.util, java.sql, java.math etc. It includes core topics like OOPs, String, Regex, Exception, Inner classes, Multithreading, I/O Stream, Networking, AWT, Swing, Reflection, Collection, etc.

2) Java EE (Java Enterprise Edition)

It is an enterprise platform that is mainly used to develop web and enterprise applications. It is built on top of the Java SE platform. It includes topics like Servlet, JSP, Web Services, EJB, JPA, etc.

3) Java ME (Java Micro Edition)

It is a micro platform that is dedicated to mobile applications.

4) JavaFX

It is used to develop rich internet applications. It uses a lightweight user interface API.

Prerequisite

To learn Java, you must have the basic knowledge of C/C++ programming language. 

Audience

Our Java programming tutorial is designed to help beginners and professionals.

Problem

We assure that you will not find any problem in this Java tutorial. However, if there is any mistake, please post the problem in the contact form.

What is coding? How it's work?

 

What is Coding?

You’ve heard tons about coding and know that it has to do with computers and how they process the information we see. However, you may not have much more than this vague understanding of the concept. Don’t feel bad. Many people don’t really understand it, though we all depend on the fundamentals of code each and every day. It’s such an important aspect of the technological landscape that, according to Fortune, former President Obama unveiled an initiative called “Computer Science for All” that has been backed by and invested in by big names, including the Turner Broadcasting Network. This movement is one that mirrors that of our nation’s schools that have been emphasizing the value of technology, computer science, engineering, and other STEM-related topics. Read on to discover what computer code is and why it is of such importance in today’s society.

What Coding Is

Coding is basically the computer language used to develop apps, websites, and software. Without it, we’d have none of the most popular technology we’ve come to rely on such as Facebook, our smartphones, the browser we choose to view our favorite blogs, or even the blogs themselves. It all runs on code.

How Coding Works

To put it very simply, the code is what tells your computer what to do. To go a bit deeper, computers don’t understand words. They only understand the concepts of on and off. The capabilities of a computer are guided by on and off switches, or transistors. Binary code represents these on and off transistors as the digits 1 and 0. An infinite number of combinations of these codes make your computer work. In order to make binary code manageable, computer programming languages were formed. These languages each serve different purposes, but they all allow programmers to translate important commands into binary code.

Each computer application needs a properly written code to know what to do. Most software has thousands to billions of lines of coded text and numbers. The code gives computers a step-by-step guide on how to function. Computers speed through reading the code to execute every online and offline task. In today’s digital world, everything from mobile phones to smart TVs and cars run using coded software. For example, the code might tell the computer to input an image and make it spin. Creating flawless code is essential to avoid 404 error pop-ups and software crashes. Debugging code is always the final step to reveal and fix coding issues.

Is Learning to Code Difficult?

Coding isn’t that hard for tech-savvy people who patiently put in the time and effort to learn. Coding gets an unnecessarily bad reputation from people who weren’t persistent enough to practice. The easiest coding languages only involve a few hundred terms and rules to remember. That’s a tiny sliver in comparison to learning a spoken foreign language. Once the easier languages are mastered, it’s relatively simple to learn other ways to code. Many programming languages use similar methods to code and debug computer applications.

Beginners starting to code must possess certain skills to be successful. Having strong attention to detail to pour over long lines of coded text is imperative. New coders need abstract thinking skills to visualize what written code will become. Novices must have problem-solving skills to persist against challenges without letting frustration win. Intuitive logical reasoning skills help coders correctly conclude why a code isn’t working right. Good writing skills are critical to creating code that appropriately conveys the intended message. Technology skills are also an obvious requirement for coders to fearlessly work with computer programs.

Popular Coding Languages to Learn

Since the 1970s, computer experts have created more than 700 different programming languages. Each language has a unique way of helping computers process huge amounts of information. Every coding language has different features and terms with some overlap. New coders shouldn’t be overwhelmed by the plethora of programming types though. There are only about a dozen programming languages that are commonly used. These include Ruby, Swift, JavaScript, Cobol, Objective-C, Visual Basic, and Perl. Let’s look at some of the major coding languages about which beginners should know.

• HTML – HTML, or hypertext markup language, is the standard way of coding web pages to showcase electronic information. Founded by Tim Berners-Lee in 1990, HTML is used to format the content, pictures, and videos featured online. HTML tells the internet browser how to display websites for an optimal user experience.
• Java – Java is an object-oriented coding language created by Sun Microsystems in 1995. Java has English-based commands used to create applications for a single computer or whole server and tiny applets for websites. Java is a popular favorite for programming mobile apps and video games, especially on Android operating systems.
• Python – Python is a server-side web and software development language started by Guido van Rossum in 1991. Python has a simple, English-like syntax to script back-end actions for applications, user interfaces, and operating systems that work well. Many platforms, including Google and NASA’s Integrated Planning System, use Python.
• CSS – CSS, or cascading style sheets, is a coding language used to specify a website’s style. Developed by Håkon Wium Lie in 1994, CSS tells internet browsers each page’s layout, background color, font size, cursor shape, and more. Crafting and maintaining solid CSS code is crucial for websites to have aesthetic appeal.
• C Language – C Language is a simple, low-level coding type initiated in 1972 by Bell Labs to build the UNIX system. Perhaps the easiest language, C has just 32 basic keywords used for scripting embedded systems, network drivers, and artificial intelligence. C language is versatile to get computer hardware communicating.
• C++ – C++ is another object-oriented programming language that expands on C to execute higher-level computer tasks. Released in 1983 by Bjarne Stroustrup, C++ organizes and stores info in bundles for more complex programs. Adobe, Microsoft Office, Amazon, and Mozilla software utilizes C++ for fast processing.
• PHP – PHP, or hypertext processor, is a coding language for web development founded in 1994 by Rasmus Lerdorf. PHP is widely used for server scripting with HTML to piece together dynamic website content. WordPress, an open-source online platform that accounts for 20 percent of websites and blogs, is notably written with PHP.
• SQL – SQL, or structured query language, is a domain-specific coding type that streams information into a database. First introduced by IBM researchers in 1974, SQL has simple syntax to run back-end web databases. SQL is used by most businesses to load, retrieve, and analyze text or numbers in their servers.

Ways to Learn How to Code

People looking to become proficient in coding for a long-term career may want a college degree. Most coding-related tech careers require at least a baccalaureate. Bachelor’s degrees require 120 education credits beyond a high school diploma or GED certificate. Find an accredited, four-year college with a slew of coding courses. Computer science majors typically get the best in-depth understanding of various programming languages. Other viable majors include information technology, computer information systems, information science, data science, web development, software engineering, and computer engineering. For careers that don’t demand a bachelor’s, consider attending a community college or technical school. Associate’s degrees in computer science and programming take only two years to complete 60 course credits.

However, paying for 24-48 months of a college education isn’t the only way to learn to code. Many of the best coders are self-taught. The internet is packed with free and paid online tutorials to master the science of coding. Great platforms, such as Coursera, Codecademy, EdX, Khan Academy, and Udacity, offer online coding classes that cost $0. Games like Minecraft, Robocode, and Lightbot teach coding skills. Free code editors, including Notepad++, Sublime Text, Bluefish, and Visual Studio Code, help beginners learn. Attending a coding boot camp can also be effective. Bootcamps are short-term, intensive training workshops available online or in person. Bootcamp providers like the Flatiron School, App Academy, Codesmith, and Wyncode usually charge a fee for three to 12 months of coding education. STEM summer camps can teach kids how to code before high school graduation too.

Careers Where Learning to Code is Helpful



According to Reader’s Digest, nearly 50 percent of jobs paying at least $58,000 require some coding skills. It’s estimated that 7 million annual U.S. job openings require knowing how to code. Competency with coding is America’s most desired job skill. Many people assume coding experts can only succeed as computer programmers. A programmer does write code to create functional software that executes tasks perfectly. Nonetheless, learning to code is helpful for hundreds of careers in wide-ranging sectors from medicine to education and finance. Here are some in-demand careers that prioritize candidates with coding abilities.

• Database Administrator – Database administrators use coding to create secure data storage files and backups. Database administrators frequently use SQL and C or C++ to fine-tune database systems for authorized access. By 2028, the employment of database administrators will increase quickly by 9 percent for 127,400 new jobs. Database administrators enjoy a mean annual wage of $89,050.
• Web Developer – Web developers are coding gurus who design the content, graphics, audio, and video features for internet sites. Web developers generally use HTML, CSS, and Java to publish top-performing websites that drive abundant online traffic. The number of web developers will surpass 181,400 by 2028 for 13 percent growth. Web developers are compensated with an $82,370 average salary.
• Information Security Analyst – Information security analysts know how to code digital software that encrypts and protects data files. Information security analysts often utilize C++, Python, and JavaScript to create tools that stop cybercriminals in their tracks. Demand will skyrocket by 32 percent for 35,500 more information security jobs. Information security analysts have median annual earnings of $99,730.
• Applications Developer – Applications developers are the creative coders behind the software that runs on computers, tablets, phones, smart TVs, and wearable tech. Applications developers use coding languages from Ruby to Scala to fill app stores with downloadable software. The hiring of applications developers will soar by 26 percent for 241,500 positions. Applications developers bring home mean income of $108,080.
• Health Informatics Specialist – Health informatics specialists design database systems for collecting, storing, and accessing patient records. Health informatics specialists know basic coding languages to develop functional, confidential EHR software for electronic recordkeeping. Faster-than-average growth of 11 percent is projected for health informatics through 2028. Health informatics specialists reap a median salary of $88,625.
• Health Informatics Specialist – Health informatics specialists design database systems for collecting, storing, and accessing patient records. Health informatics specialists know basic coding languages to develop functional, confidential EHR software for electronic recordkeeping. Faster-than-average growth of 11 percent is projected for health informatics through 2028. Health informatics specialists reap a median salary of $88,625.
• Instructional Designer – Instructional designers in today’s digital age must know coding to develop an engaging curriculum for K-16 students to learn. Instructional designers code with C, PHP, Java, and others to create interactive course teaching materials. Instructional designers will experience 6 percent growth to 193,000 total jobs. Instructional designers earn an average yearly paycheck of $69,180.
• Digital Marketing Manager – Digital marketing managers with coding skills can elevate their advertising campaigns to attract better business sales. Digital marketing managers use the internet as their medium for reaching potential customers with traffic to well-coded websites. The job outlook for digital marketing managers shows an 8 percent uptick for 20,900 new openings. Digital marketing managers have mean profits of $149,200.

• The benefits of learning to code are actually quite vast. No longer do we live in a time when only tech professionals are using this useful language. Being able to utilize the commands of code yourself will enable you to have more control of the technology on which you depend.

  For example, whether it’s for personal or professional use, more and more people are writing blogs and creating websites to share their message or skills with the world. Being able to understand basic code would allow you to make tweaks to the design of your site without having to pay a webmaster to do it for you or to wait for someone from IT to take care of the ticket you submitted ages ago. Knowledge of code can take you even further if you decide to pursue it. Some in-depth study will provide you with the skills to build a website to your own specifications and to fit your desired needs.

  If you find you have a passion for technology and a talent for navigating the in-depth nature of computer languages, you could go on to become a professional coder and work for a tech company or even start your own business venture or launch a best-selling idea. The job market is opening up for technologically savvy professionals who have marketable skills in coding.

C Program to Calculate Sum & Average of an Array

                         

This is a C Program to calculate the sum & average of an array.

Problem Description

We have to write a program in C such that we are reading an array of N elements and then we are going to calculate the sum and average of those N elements and display it to the standard output or screen.

Expected Input and Output

If we are entering 5 elements (N = 5), with array element values as 10, 20, 30, 40 and 50 then,
1. Sum of Elements of the array will be: 10 + 20 + 30 + 40 + 50 = 150
2. Average of Elements of the array will be: 150 / 5 = 30 

Problem Solution

Fundamentally, an array is a data structure containing a collection of values or variables. The simplest type of array is a linear array or one-dimensional array. An array can be defined in C with the following syntax:
int Arr[5] = {10, 20, 30, 40, 50};
/* here 10,20,30,40,50 are the elements at indices 0,1,2,3,4 respectively */

In this example, array Arr is a collection of 5 integers. Each integer can be identified and accessed by its index.

The indices of the array start with 0. So, the first element of the array will have index 0, the next will have index 1 and so on. For example, if we have to add 20 to the 2nd element, then the syntax will be:
Arr[1] = Arr[1] + 20; (we can also use Arr[1] += 20;)

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For the solution, we will first construct an array with user-defined length and then, we will find its sum and average.

The formula for the average of the array will be:
average=Σ(elements of the array)/number of elements in the array

Simply, we will add all the elements of the array and then divide it with the number of the elements to find the averag