Machine Learning: Evolution Types Algorithms Applications Software

Machine Learning

An important method of data analysis that helps in automating the analytical model building is known as Machine Learning.

Machine Learning, abbreviated as ML, is a branch of Artificial Intelligence; and is based upon the idea that systems can make decisions, identify patterns, and learn from data with minimum human intervention.

History and Evolution of Machine Learning

1950 –The ‘Turing Test’ is created by Alan Turing to determine whether a computer has real intelligence or not.

A PC must be able to fool a human being into believing that it is also a human being, to pass a test.

1952 – The 1st computer program was written by Arthur Samuel.

1957 –Rosenblatt designed the 1st neural network for PCs that is able to simulate the thought process of the brain of a human being.

1967 – The ‘nearest neighbour’ algorithm was written to allow the PCs a very basic pattern recognition.

1979 –‘Stanford Cart’ was invented by the students at Stanford University that is able to navigate obstacles in a room on its own.

1981 –Gerald Dejong introduced the important concept of EBL, i.e. Explanation Based Learning.

In EBL, a PC analyses training information and creates a rule that it can generally follow by discarding unimportant information.

1985 – NetTalk was invented by Terry Sejnowski, which was aimed to pronounce words in the same manner a child/baby does.

1990s – Work on ML changed from a knowledge-driven approach to a more data-driven approach.

Scientists in this era started creating programs for PCs to analyse massive amounts of information and ‘learn’ or draw conclusions – from the results.

1997 –The chess world champion is defeated by IBM’s Deep Blue.

2006 –‘Deep Learning’ term was coined by Geoffrey Hinton for explaining new algorithms that allow computers distinguish and ‘see’ texts and objects in videos and images.

2010 –The Microsoft Kinect was able to track down twenty human being features at a rate of thirty times per sec.

It allowed individuals to interact with the PC via gestures and movements.

2011 – Watson of IBM defeats its human competitors at Jeopardy.

2011 – Google Brain is developed and its neural network is able to categorize and discover objects pretty much in the same manner a cat does.

2012 –X Lab of Google develop a ML algorithm which is itself capable for browsing videos of YouTube and can easily and quickly identify the YouTube videos that contain cats.

2014 –DeepFace is developed by Facebook which is a software algo that allows a person to verify or recognize individuals on photographs to the same level as human beings can do.

2015 –Amazon launches its own ML platform this year.

2015 – Distributed Machine Learning Toolkit is created by Microsoft, that enables the efficient distribution of ML problems across multiple PCs.

2015 – Over three thousand Robotics and AI researchers, who were endorsed by Steve Wozniak, Elon Musk, and Stephen Hawking, signed an open letter warning of the danger of autonomous weapons that engage and select targets without human beings intervention.

2016 –A professional player at the Chinese board game ‘Go’ is defeated by the AI algo of Google.

This Chinese board game is many times harder as compared to Chess and considered as world’s most complex board game.

Google Deepmind developed AlphaGo algo and it managed to win 5 out of 5 games in the Go competition.

Why Machine Learning?

The nearby limitless quantity of affordable data storage, available data, and the growth of more powerful and less expensive processing has propelled the growth of machine learning.

Why Machine Learning?

Now, there are several industries are developing more and more robust ML models capable of analysing more and bigger complex information while delivering more accurate, and faster results on vast scales.

ML tools enable industries to more quickly identify potential risks and profitable opportunities.

The practical applications of ML drive business results that can dramatically effect an industry’s bottom line.

New techniques in this field are expanding and evolving rapidly the application of ML to nearly limitless possibilities.

Companies which depend on large quantities of information – and require a system to analyse it accurately and efficiently, have embraced ML as the best way to plan, strategize, and build models.

Industries that use Machine Learning –

1 Healthcare

The proliferation of wearable devices and sensors which monitor everything from steps and pulse rates walked to sugar and oxygen levels and even patterns of sleeping have developed a significant amount of information which enables doctors to access the health of their patients in real-time.

1 new ML algo detects cancerous tumouron mammograms; another analyse retinal photographs to diagnose diabetic retinopathy, a third can identify skin cancer.

2 Government

Systems that use ML enable the officers to use information to adapt to rapidly changing scenarios and to predict potential future situations.

ML can help improve cyber intelligence and cybersecurity, reduce failure rates, predictive maintenance, logistics management, optimize operational preparedness, and support counterterrorism efforts.

3 Marketing and Sales

ML is even revolutionizing the marketing industry as several companies have successfully implemented ML and AI (Artificial Intelligence) to enhance and increase consumer satisfaction by more than ten per cent.

According to Forbes, fifty-seven per cent of enterprise executives believe that the most significant growth advantages of AI and ML will be improving consumer support and experiences.

Social media websites and E-commerce use ML to analyse your search and buying history –and based on your past habits, make recommendations on other items to purchase.

Several experts theorize that the future of retail will be driven by ML and AI as deep learning business applications become even more adept at using, analysing, and capturing information to personalize people’s shopping experiences and create customized targeted campaigns of marketing.

4 Transportation

Accuracy and efficiency are 2 key points for profitability within the transportation sector, so is the ability to mitigate and predict potential problems.

Modelling functions and data analysis of machine learning perfectly dovetail with businesses within the freight transport, public transportation, and delivery sectors.

ML uses algo for finding factors that negatively and positively impact the success of supply chain, making ML an important component within supply chain management.

Within logistics, ML facilities the schedulers’ ability for optimizing QC processes, routing, rating, that improves efficiency and saves money.

The ability of ML to analyse 1000s of data points simultaneously and apply algo more easily and quickly than any human being enables ML for solving problems that individuals haven’t yet identified.

5 Financial Services

The insights that are provided by ML in financial services industry allow investors to know when to trade or to identify new opportunities.

Data mining informs cyber surveillance and pinpoints high-risk clients to mitigate and find signs of fraud. ML can help assess risk for insurance and loans underwriting and calibrate financial portfolios.

The future of ML and AI in financial services market include an ability to analyse stock market movement and to evaluate hedge funds to make financial recommendations.

ML may render security questions, passwords, and usernames by taking anomaly detection to the next level: voice or facial recognition, or other biometric data.

6 Oil and Gas

AI and ML are already working to streamline oil distribution to shrink costs and increase efficiency, predict refinery sensor failures, analyse mineral deposits in ground, and find new energy sources.

ML id revolutionary the oil and gas industry with its drill floor automation, reservoir modelling, and case-based reasoning, too. And above all, ML is making its efforts to make this dangerous industry safer.

7 Manufacturing

ML is no stranger to the vast manufacturing industry, either.

Its applications in this sector are about accomplishing the target of improving operations from conceptualization to the final delivery, significantly increasing inventory turn, improving predictive maintenance, and reducing error rates.

Unlike the transportation industry, ML has helped several companies improve logistical solutions that include inventory management, supply chain, and assets.

ML also plays a vital role to enhance OEE i.e. overall equipment effectiveness by measuring the quality, performance, and availability of assembly equipment.

Types of Machine Learning

As with any other thing in this world, there are many methods to train ML algo, each with their disadvantages and advantages.

We must first look at what kind of information each type of machine learning ingest for understanding pros and cons of them.

There are 2 kinds of data – unlabeled data and labeled data in machine learning.

Labeled data has both the output and input parameters in an entirely machine readable pattern, but to begin with, it requires a lot of human beings to label the data.

Unlabeled data is the data that has only 0 or 1 parameters in a machine readable form.

This requires more complex solutions but negates the need for human being labor.

There are some kinds of ML algo that are used in very specific use cases, but there are mainly 3 main types of machine learning which are used today.

1 Supervised Learning

One of the most basic types of machine learning is supervised learning.

The machine learning is trained on labeled data in this type of machine learning.

Supervised learning is exceptionally powerful when used in the correct circumstances, even though the data needs to be labeled accurately for this method to work appropriately.

The machine learning algo is given a small training dataset to begin with, in this type of machine learning.

This training dataset serves to give the algo a basic idea of the data points, solution, and problem to be dealt with; and this is a smaller set of the larger dataset.

The training dataset provides the algo with the labeled parameters needed for the problem, and it is also very similar to the final dataset with respect to characteristics.

The machine learning algo then finds the relationship between the parameters given, and establishes a cause and effect relationship between the variables that are present in the dataset.

The machine learning algo has an idea of the relationship between the output and the input and how the data works, at the end of the training.

The solution is then used for the final dataset, that it learns pretty much the same way as the training dataset.

This essentially means that the supervised machine learning algo will continue to improve even after they are deployed.

It then discovers new relationships and patterns as it trains itself on more new data.

2 Unsupervised Learning

This type of machine learning has the benefit of working with unlabeled data.

This implies that the labor of human beings isn’t required for making the dataset machine readable, which allow much bigger datasets to be worked on the program.

The labels allow the ML algo to find the exact nature of the relationship between any 2 data points in supervised learning.

But, unsupervised learning results in the creation of hidden structures as it does not have labels to work off of.

Relationships between several data points are established in an abstract way, with 0 input needed from humans.

The development and creation of hidden  structures is what make the unsupervised learning algo more and more versatile.

This type of machine learning algo can adapt to the data by dynamically changing hidden structures, instead of a set and defined problem statement.

Unsupervised machine learning allows more post deployment development as compared to the supervised learning algo.

3 Reinforcement learning 

This type of machine learning takes inspiration directly from how humans learn from data in their real life.

Reinforcement learning features an algo that learns from new scenarios using a trial and error method.

Non-favorable outputs are punished or discouraged and favorable outputs are reinforced or encouraged.

This type of machine learning works by putting the algo in a work environment with a reward system and an interpreter on the basis of the psychological concept of conditioning.

The result of the output is given to the interpreter in each iteration of the algo, that decides if the outcome is favorable or not.

The interpreter reinforces the solution by providing a reward to the algo in case the program finds the right solution.

In unfavourable case, the algo is forced to reiterate unless it is able to find a better solution.

Therefore, the reward system is directly tied to the effectiveness of the result in most of the cases.

In typical reinforcement learning use cases, the solution is not an absolute value such as finding the shortest distance i.e. displacement between 2 points on the map.

Instead, it is expressed in a % value as it takes on a score of effectiveness.

The higher this %, the more reward is given to such algo. Therefore, this program is suitable for giving out the best possible solution for the best possible reward.

Machine Learning Algorithms

When you are crunching information to model business decisions, you are most probably using unsupervised learning methods and supervised learning methods.

One of the most hot topics at the moment is semi-supervised learning methods in an area of photograph classification where there are big datasets with less labeled examples.

Mostly, algo are grouped in terms of their similar function. Let’s say, for instance, neural network and tree based methods inspired methods.

My opinion is that it is the most apt way of grouping algo and this same approach will be used in this article. But as anything in this world isn’t perfect, so is this method.

There are many algo that can easily fit into categories such as Learning Vector Quantization, which is both an instance-based method as well as a neural network inspired method.

There are also many categories present that have the same name which is used to describe the problem and the class of algorithm such as clustering or regression.

1 Regression Algorithms

Regression algorithms are concerned with modelling the relationships between different variables which is refined again and again by using an error measurement in the predictions made by the model.

These kind of methods have been co-opted into statistical machine learning and are a workhorse of statistics.

This may sound as confusing because we can use regression methods to refer to the class of algorithm and the class of problem.

The most famous regression algo are –

  • LOESS i.e. Locally Estimated Scatterplot Smoothing
  • MARS i.e. Multivariate Adaptive Regression Splines
  • Stepwise regression method
  • Logistic regression method
  • Linear regression method
  • OLSR i.e. Ordinary Least Squares Regression

2 Instance – based Algo

The instance-based learning is a model on problem with examples of training data which are deemed required or important to the model.

This method generally compare new data to the database using a similarity measure for finding the best match and making a prediction which helps in building up a database of example data.

Therefore, for this reason, this method is also known as memory-based learning and winner-take-all method.

The main focus in this model is put on the similarity measures that are used between instances and the representation of the stored instances.

The most famous instance-based algo are –

  • SVM i.e. Support Vector Machines
  • LWL i.e. Locally Weighted Learning
  • SOM i.e. Self-Organising Map
  • LVQ i.e. Learning Vector Quantization
  • kNN i.e. k-Nearest Neighbour

3 Regularization Algorithm

The regularization algo is an extension made to other method which penalizes models that are highly complex, and favors models that are simpler and better at generalizing.

The most famous regularization algo are –

  • LARS i.e. Least-angle regression
  • Elastic Net
  • LASSO i.e. Least Absolute Shrinkage and Selection Operator
  • Ridge Regression

4 Decision Tree Algorithm

This algorithm construct a decisions model that is based on the actual values of attributes in the information and data.

Decision fork under a prediction decision in tree structures is made for a given record.

Such decision trees are trained on information for regression and classification problems.

Decision trees are a big favorite in machine learning as they are often accurate and fast.

The most famous decision tree algo are –

  • Conditional Decision Trees
  • M5
  • Decision Stump
  • CHAID i.e. Chi-squared automatic interaction decision
  • C5.0 and C4.5 – These are different versions of a powerful approach.
  • ID3 i.e. Iterative Dichotomiser 3
  • CART i.e. Classification and Regression Tree

5 Bayesian Algorithm

Bayesian algo are those which explicitly apply Bayes’ Theorem such as regression and classification.

The most famous Bayesian algo are –

  • BN i.e. Bayesian Network
  • BBN i.e. Bayesian Belief Network
  • AODE i.e. Averaged One Dependence Estimators 
  • Multinomial Naive Bayes
  • Gaussian Naive Bayes
  • Naive Bayes

6 Clustering Algorithm

Clustering algo such as regression describes the class of methods and the class of problems.

Such methods are generally organized by the approaches of modelling like hierarchical and centroid-based.

All such methods are attentive for using the inherent structures that are present in the information or data to best organize such data into groups of max commonality.

The most famous type of clustering algo are –

  • Hierarchical Clustering 
  • EM i.e. Expectation Maximisation
  • k – Medians
  • k – Means

7 Association Rule Learning Algorithm

These extract rules which best explain observed relationships between the different variables present in data.

Such rules can discover crucial and commercial useful associations in big multi dimensional data sets which can be easily exploited by an institution.

The most famous association rule learning algo are –

  • Eclat algo
  • Apriori algo

8 Artificial Neural Network Algorithm

Such algo are inspired by the function and the structure of biological neural networks.

These are a class of pattern matching which are mostly used for classification and regression problems but are also a massive subfield consists of 100s of variations and algorithms for all types of problems.

The most famous types of artificial neural network algo are –

  • RBFN i.e. Radial Basis Function Network
  • Hopfield Network
  • Stochastic Gradient Descent
  • Back – Propagation
  • MLP i.e. Multilayer Perceptron
  • Perceptron

9 Deep Learning Algorithm

The modern update to artificial neural network which exploit massive cheap computation are deep learning methods.

These are concerned with building more complex and much larger neural networks and different methods are concerned with very big data sets of labelled analogue info, such as video, audio, text, and images.

The most famous types of deep learning algo are –

  • DBN i.e. Deep Belief Network
  • DBM i.e. Deep Boltzmann Machine
  • Stacked Auto – Encoders
  • RNNs – Recurrent Neural Networks
  • CNN – Convolutional Neural Network

10 Dimensionality Reduction Algorithm

Dimensionality Reduction Algorithm seek and exploit the inherent structure that is present in the information and data, but if we talk about this case, an unsupervised way to describe or summarize information using less data.

This can prove beneficial in simplifying data or visualizing dimensional data that can be used in a supervised learning method.

Many such methods can be used in regression and classification.

  • FDA i.e. Flexible Discrimant Analysis
  • QDA i.e. Quadratic Discriminant Analysis
  • MDA i.e. Mixture Discriminant Analysis
  • LDA i.e. Linear Discrimant Analysis
  • Projection Pursuit
  • MDS i.e. Multidimensional Scaling
  • Sammon Mapping
  • PLSR i.e. Partial Least SquaresRegression
  • PCR i.e. Principal Component Regression
  • PCA i.e. Principal Component Analysis

11 Ensemble Algorithm

Ensemble algo works on the models comprised of many weaker models whose predictions are combined in some way for making the complete prediction and are independently trained.

The many famous types of ensemble algo are –

  • Random Forest
  • GBRT i.e. Gradient Boosted Regression Trees
  • GBM i.e. Gradient Boosting Machines
  • Stacked Generalization
  • Weighted Average (Blending)
  • AdaBoost
  • Bootstrapped Aggregation (Bagging)
  • Boosting

Applications of Machine Learning

1 Image Recognition

One of the most common and prominent application of ML in today’s world is Image Recognition.

It is used for identifying digital images, places, persons, identify objects etc.

The most famous use case of face detection and image recognition is Automatic friend tagging suggestion.

FB provides us with this feature that whenever we upload a picture with our friends, it automatically get a suggestion for tagging for the people in the picture.

The technology behind this is machine learning’s recognition algo and face detection.

This technique of FB’s image recognition is based on its project ‘Deep Learning’, that is responsible for person identification and face recognition in the image.

2 Speech recognition

While you are using the search engine Google, we always get an option of ‘Search by Google’.

This feature comes under speech recognition, and is a very famous application of machine learning.

The process of converting instructions of voice to text, is known as ‘Computer Speech Recognition’ and ‘Speech to text’.

These days ML algo are used in several applications of speech recognition.

Alexa, Cortana, Siri and Google Assistant are using this technology for following the voice instructions.

3 Traffic Prediction

Whenever we want to visit a new place, all of us take help of the Google Maps, that helps us in showing the shortest route to your destination and predicts the traffic conditions of various routes.

Traffic conditions such as whether the traffic is heavily congested, slow-moving, is the traffic cleared; are shown with the help of 2 methods –

  • The average time that has been taken by people on the same route on the past days and at the same time.
  • Real time location of the vehicle form google map sensors and apps.

Every person who is using this app is helping Google Map make it better.

It takes data from the users and sends it back to the database for improving its database.

4 Product recommendations

ML is widely used by several entertainment and e-commerce companies like Netflix and Amazon for product recommendations to the users.

You must have seen that when you search for a product on E-Commerce websites such as Amazon, then you start seeing similar ads of products from different companies on the browser you are using for surfing; and this is because of ML.

The search engines understand the interest of the users using several ML algo; and then they suggest the products as per consumer interest.

Similarly, we find recommendations for movies, TV shows, and web series when we view a movie, TV show, and Web series belonging to a particular genre.

5 Self-driving cars

One of the most interesting applications of ML are self-driving cars and ML plays a big, big role in this aspect.

Tesla, the company of Elon Musk is working on the self-driving cars and are using the unsupervised way of learning for training cars to detect objects and people while driving.

6 Malware Filtering and Email Spam

Whenever we receive an email, it is automatically filtered as spam, normal, or important.

We always receive spam email messages in the spam box, and important email messages in the ‘Important’ folder.

This is done by ML and some of the filters used by Gmail are –

  • Permission filters
  • Rules-based filters
  • General backlists filter
  • Header filter
  • Content filter

7 Virtual Personal Assistant

Today, we have several virtual personal assistants such as Alexa, Cortana, Google Assistant, and Siri.

As the name implies, such virtual personal assistants help us in finding info by using our voice instructions.

These personal assistants help us by following our voice instructions and commit jobs such as scheduling an appointment, opening an email, calling someone, or playing music, etc.

For such operations, ML algo play a very important role.

These personal assistants record our voice, send it on the cloud over a server, and decode it using machine learning algo and act accordingly.

Real Implementation of Machine Learning

1 Medical diagnosis

ML can be effectively used in the tools and techniques which can help in the diagnosis of diseases.

It is also used for clinical parameters analysis and its combination for the prognosis prediction of disease progression for the outcome research, for patient monitoring and for therapy planning.

This is of the most successful real-time implementation of the ML methods.

It can also prove helpful in the integration of computer based methods in the healthcare sector.

2 Statistical Arbitrage

Arbitrage means automatic trading strategies which involve a high no of securities and are of a short-term.

In this, the user focuses on implementing the trading algo for securities on the basis of quantities such as the general economic variables and historical correlations.

The methods of ML are applied for obtaining an index of arbitrage strategy.

We apply the Support Vector Machine and linear regression to the prices of a stream of stocks.

3 Learning associations

The process of developing insights into the several associations between the items or the products are known as learning associations.

A good example of the same is how many unrelated products can be associated with each other.

One of the major applications of ML is studying the relations between several products that people buy.

If a user purchases a particular product, then he/she should be shown similar products as per his/her choices and preferences between both products.

Whenever a new product is launched in the marketplace, these are associated with the old products for increasing the sales of the new ones.

4 Prediction

ML can be effectively used in the prediction system.

Consider an example of loan system, for computing the probability of a fault, the system might need to classify the information that is available in groups.

The probability of the fault can be easily calculated once the classification has been done. Therefore, making predictions is one of the most useful applications of ML.

5 Extraction

The process of extracting structured info from unstructured data is known as extraction and is one of the most useful and beneficial real-time implementations of machine learning.

Emails, business reports, blogs, articles, and web pages are some examples.

A relational database is used for maintaining the output that is produced by the extraction of information.

The extraction process takes documents as inputs and outputs the structured info.

Future of Machine Learning 

  • Machine Learning will be a very important part of all the Artificial intelligence systems  whether small or large.
  • There is a very strong possibility that machine learning might be offered aa a cloud-based service which can be called as Machine Learning-as-a-Service i.e. MLaaS. This is because of machine learning assuming increased value in business applications.
  • Connected artificial intelligence systems will enable machine learning algo for continuously learning, based on the newly emerging info on the world wide web.
  • There will be a very huge rush among the vendors of hardware for enhancing CPU power and accommodating the machine learning data processing. Most probably, vendors of hardware will be redesigning their machines for doing justice to the power of Machine Learning.
  • ML will be helping machines for making better sense to the meaning and context of data.
  • Use of multiple technologies in machine learning – It is no secret that the emergence of Internet of Things has proved advantageous to machine learning in several ways. The use of various technological strategies is in current use of Machine Learning; and in the coming time; more and more collaborative learning by using different technologies is probable.
  • Personalized Computing Environment –Developers might have access to various API kits for designing and delivering more intelligent apps. Therefore, this effort is similar to assisted programming in a way. Developers will easily embed vision, speech, or facial-recognition features into the systems with the help of these API kits.
  • With the help of quantum computing, the speed of the execution of the machine learning algo will be increased greatly. This should be the next conquest in the field of the research of machine learning.
  • Higher business outcomes will occur due to the future advancements in unsupervised machine learning algo.
  • Tuned Recommendation Engines – The services that will be using machine learning will become more relevant and accurate. For instance,the recommendation engines will be more closer and relevant to a person’s personal tastes and preferences.

Software for Machine Learning

1 TensorFlow

TensorFlow is the standard name that is given to the machine learning in the data science industry.


It facilitates building of both deep learning and machine learning solutions with the help of its extensive interface of CUDA GPUs.

A multi-dimensional data array i.e. Tensor is the most basic type of data of TensorFlow.

It can be seen as an open source tool kit which can be used for building ML pipelines.

This can help you in building scalable systems for processing information.

It also provides functions and support for several applications of machine learning like Reinforcement learning, NLP and computer vision. It is one of the most popular tools of ML for beginners.

2 Shogun

An open-source and famous ML software is Shogun.

About Shogun

It is written in C++ language, and also supports several other languages such as Ruby, C#, Scala, R, and Python. Some of the algo that are supported by Shogun are –

  • Linear Discriminant Analysis
  • Hidden Markov Models
  • Clustering Algo
  • Dimensionality reduction
  • Support Vector Machines

3 Apache Mahout

This is an open-source ML software that is focused on both classification and collaborative filtering; and such implementations are an extension of the platform of Apache Hadoop.

While this software is still in the progress mode, the no of algo which are supported by this software have grown significantly.

It uses the Map/Reduce paradigms as it is implemented on the top of Hadoop. Some of the most unique features of this software are –

  • It provides native solvers for CUDA, GPUs, and CPUs accelerators.
  • It also provides a distributed linear algebra framework and expressive Scala DSL for deep learning computations.

4 Apache Spark MLlib

Spark provides many advanced ML features with the help of its ML library and is also a very robust data streaming platform. Spark provides a scalable ML platform with the help of its several APIs which allow users for implementing ML on real-time information.

Any Hadoop source can be seamlessly integrated with the help of its Machine learning library and algorithms.

You are also allowed to perform iterative computations with the help of Spark, through which you can definitely achieve good results for your algo. Some of the algo that are supported by it are –

  • Random Forests, Decision Trees, etc.
  • Topic Modelling, LDA, Gradient Boosting
  • Survival Analysis, Linear Regression
  • Logistic Regression, Naive Bayes, Classification

5 Oryx 2

Oryx 2 uses of Lambda Architecture for large scale and real-time ML processing.

Oryx 2 was built on top of the Apache Spark architecture which involves functions for building applications and rapid-prototyping. This software facilitates end-to-end model development for clustering, regression, classification, collaborative filtering operations.

This software has the following 3 tiers –

  • The 1st tier is of a generic lambda tier which provides serving and speed layers, which are not specific to procedures of ML.
  • The 2nd tier gives machine learning abstractions to select the hyper parameters.
  • The 3rd tier gives an end to end implementation of the machine learning apps.


The deep learning platform of gives a scalable multi layer artificial neural network.

This ANN consists of various parameters and components that can be changed according based on the info provided.

It also comprises of adaptive learning rate and rate annealing for yielding highly predictive output.

Other versions of support Recurrent Neural Networks and Convolutional Neural Networks.

7 Pytorch

Pytorch is an advanced deep learning framework developed by FB. The most important features of Pytorch include Tensors and Deep Neural Networks.

A person can develop rapid prototyping for research with the help of Pytorch. Moreover, you can also build pipelines of software using Pytorch.

Uber’s probabilistic programming language is built with this software for machine learning.

Using this software, you can also develop dynamic graphs for accelerating your ML processes.

It also provides your code the ability of information parallelism.

8 RapidMiner

RapidMiner gives a comprehensive and integrated environment for carrying out various tasks such as predictive analysis, text mining, deep learning, machine learning, and data preparation.

It is also famous for its fast speed to avoid risks, reduce costs, and drive revenues.

Limitations of Machine Learning

Machine Learningisn’t perfect with all its benefits to its popularity and powerfulness. The following are the limitations of machine learning –

1 Data Acquisition

ML requires big data sets to train on, and these should be of good quality, and should be unbiased or inclusive.

There may be times where it must wait for new information or data sets to be generated.

2 Resources and Time

Machine learning needs enough time to let the algo develop and learn enough for fulfilling their purpose with a considerable amount of relevancy and accuracy.

It also requires huge resources for functioning and this essentially means additional requirements of computer power for an individual.

3 Results interpretation

Other major challenge that ML faces is the ability to precisely interpret results generated by the algo.

An individual should carefully select the algo for your purpose.


Most experts say ‘YES’ when posed with the question that ‘Is ML really worth all the hype surrounding it?’

Understanding the basics of AI and ML is a must, must thing for any person who is working in the tech domain these days.

The complete working knowledge of this technology is needed to stay relevant in this ever-changing digital world, due to the pervasiveness of machine learning.

Big companies are now in the middle of the adoption curve for ML and AI, mainly due to the exponential advancements and accessible cloud platforms in this field.

This makes machine learning an exciting career option for those people who have the experience and capability to take it up.

Since this field works as a combination of logical thinking, computer science, and statistics; it is heavily varied in what it can offer to new variants.

Moreover, a wide range of positions such as AI developers, machine learning engineers, and data scientists offer choices to aspirants across different verticals.

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C++ program for student details using array of objects

Array of Objects in C++

Array of Object is similar to creating array or integer, character or any primitive data types.


Array of integer is

int age[5]={1,2,3,4,5};

Similar way array of Student is

Student s[5]={studentObj1,studentObj2,studnetObj3,studentObj4,studentObj5};

Student is class Name

s is array of objects of 5 elements

To initialize student object two methods are used here

  1. Initialize student object from constructor
  2. Create a function to get input from user to initialize student object.

Example: C++ program for student details using array of objects

  1. Create a class name student
  2. Declare necessary fields for student as name,age and rollno as a private member of class.
  3. In public section create constructor to initialize value from it
  4. also create a show function to show/ print user data.

To copy local array variable name to Student class name strcpy() is used


Get User Input for Student Objects

  1. create a class student
  2. declare fields of student as private data member
  3. create a function getStudent() to get input from user and assign it to student data members
  4. create a function showStudent() to show student object details to user.
  5. create a main method and define size of student object
  6. loop student object to take input from user
  7. loop student objcet to show student object details from array of object.


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  4. Command Line Argument
  5. Local vs Global Object in C++
  6. Local and Nested Classes in C++
  7. Arithmetic operations using switch case
  8. Switch Case in C Programming
  9. Constructor in C++
  10. Know more about C++

Categories C++

How to deploy and run a war file in XAMPP tomcat

What is XAMPP?

XAMPP full form is Cross Platform(X), Apache (A), MySQL (M), PHP (P) and Perl (P).

XAMPP Control Panel allows use to Start and Stop Module services

XAMPP has following modules

  1. Apache
  2. MySql
  3. FileZilla
  4. Mercury
  5. Tomcat

To run PHP files we have to start Apache server. To access MySql Database we start MySql Services.

Similar way to run Java web applications (war files) XAMPP included Tomcat Server

Tomcat is a developed by Apache so XAMPP also included this.

Create a WAR File in NetBeans

  1. Create a new web Project
  2. Clean and build the project

This will create a war file.

We have create a war file a.war and now want to run in tomcat server provided with XAMPP.

We created war file using netbeans IDE there are many IDE available for java/j2ee you can easily choose any one of them

You can dowload XAMPP from here

I have developed a jsp servlet application in netbeans8.2 I want to give this code to UI developer to enhance the UI.

On his/her laptop they don’t use netbeans IDE. They have to work only with jsp files.

so how to run apache tomcat server in xampp ?

Lets see step by step how to configure war file with database in XAMPP

XAMPP Start/ Stop Modules

click on start to start Xampp Server’s Apache, MySql and Tomcat module here tomcat is not dependent on Apache or Mysql.

If your war is using mysql then you have to start mysql server and if you are using phpMyAdmin for MySql then Apache is needed

XAMPP Control Panel
Fig: XAMPP Control Panel

Add a Tomcat user in tomcat-users.xml

click on config you will get a drop down menu on that menu second option is tomcat-users.xml click on that

Creating tomcat user in XAMPP
Fig: Creating tomcat user in XAMPP

above file will open on notepad there find

the symbol <!– and –> is xml comment content written inside this is example of creating user with password and roles.

We have to create a user to manage tomcat page to deploy projects for that we create a user admin with password admin and with different roles as below.

Copy and paste this user detail after the comment line in tomcat-users.xml file as below

Fig: Creating user in tomcat

Next click on Admin it will open Apache Tomcat in web browser as below

Tomcat Server Home
Fig:Tomcat Server Home

On click on manage app a popup box will open and ask for user username and password we have provided username admin and password admin provide detail in popup window.

after authentication it will show page as below

below this page there are option to upload a directory or war file in tomcat use WAR file to deploy option and select war file and click on deploy

Tomcat Web Application Manager
Fig: Tomcat Web Application Manager

After click on uplaod it will upload war file and show uploaded war in application manager as below

Fig: Upload WAR file in XAMPP Tpmcat

click on you war filename to run it.

First Page of my site running war file
Fig: First page after running WAR file

Thats it 🙂

Read More

  1. Example of JSP Servlet using web.xml
  2. Example of JSP Servlet using annotation
  3. PHP MySQL CRUD Tutorial
  4. Export and Import MySql Database using phpMyAdmin

Polymorphism in PHP with Example

Polymorphism in PHP is one of the important concept in OOPs.

Polymorphism is a Greek word. Polymorphism is created by two different words poly (means many) and morph (means forms).

It is another functionality of OOPS. In programming language two types of Polymorphism are there.

  1. Function overriding (Run time Polymorphism)
  2. Function Overloading(Compile time Polymorphism)

Function Overriding in PHP

Overriding is happened only in child class.

If parent class declares a function and child class wants to override parent class function then function overriding means run time polymorphism happened.

Polymorphism example in PHP

Here are are taking a simple example.

Created a class parentclass it has a method name().

created another class childclass it inherits the feature of parentclass.

child class has also the same method name().

Defining the same method in child is know as method overriding.


Calling show method of Shape
Shape is no defined
Calling show method of Circle
Showing a Circle
Calling show method of Rectangle
Showing a Rectangle
Calling show method of Square
Showing a Square
Calling show method of Triangle
Showing a Triangle

Polymorphism example to call different methods in PHP

Here we have taken example of Shapes.

Shape is a base class with a method show().

Child classes of Shape are Circle, Rectangle, Square and Triangle.

all have a over ridden method show().

Polymorphism Example in PHP


Calling show method of Shape
Shape is no defined
Calling show method of Circle
Showing a Circle
Calling show method of Rectangle
Showing a Rectangle
Calling show method of Square
Showing a Square
Calling show method of Triangle
Showing a Triangle

Read More

  1. PHP Inheritance

Changing the default value of column in MySQL

After create table if you want to change default value for any column of table the we can use alter command in following way



above query will set default value for column_name to provided default value.

Java Servlet JSON Response in JSP using Gson

In Modern web application We get data from server in JSON format and display data in jsp or html page using Front end library or frameworks like Angular, React etc.

This is similar for mobile app development.

Here we will see how we can get JSON Response from servlet and show in jsp.

What is JSON ?

JavaScript Object Notation (JSON) is a lightweight format for interchange data.

  • JSON is a Good Human readable format
  • JSON is derived from JavaScript
  • JSON is text based format for representing structured data
  • JSON is key value pair data
  • JSON is language independent

These are features of JSON.

JSON can be used to interchange data between different platform.

Due to lightweight it is very fast to exchange information between different application i.e. web based applications.

So knowing how to use JSON with servlet will good

Creating Java web project in NetBeans

We already know how to create simple projects with netbeans see project creating with maven and creating simple web project in netbeans.

We created a Java Web project ServletJson to get Response from Servlet JSON

Project Explorer

Netbeans Project Explorer

In above project

  1. Created Pojo for Studnet
  2. Created Pojo for Address
  3. Created Pojo for Subject
  4. Created JsonResponse Servlet
  5. created index.jsp

Creating Classes and Pages In Netbeans


This file contains a link on click that will goto url


<%@page contentType=”text/html” pageEncoding=”UTF-8″%>

Here we want to send json data response from servlet.
Here a link is available in jsp page on link click it is calling /jsonResponse url where JsonResponse’s doGet() method is get called.

How to set HttpServletResponse set body json

To generate json response we are setting response.setContentType("application/json"); that is used to set response as application/json.

Here response is reference variable of HttpServletResponse.

In servlet student object is created with name gender mobile number and three subjects marks. After creating object we are converting objects to json using gson library.

response.setContentType("application/json"); is used to send response as json.

response.setCharacterEncoding("utf-8"); is character encoding method.

We have created student object which contains students id,name,gender,address, mobileNo and set of subjects and address.

We send this data to jsp using Gson Object.

The method gson.toJson(student); is converting object to json string

then we are passing json data to PrintWriter object to write in browser

This class contains student data like id, name , gender ,address, mobile number and multiple subjects mark.

contains id and name of subject.


contain address fields id , street, city,state and country fields


This is index.jsp page after running the project on click on link it will show json data in browser

Jsp Page to generate JSON Response

java servlet return json

JSON Response In JSP page from Servlet

java servlet return json

Above java servlet return JSON response.

Downlaod above program from here

Know More About Nationalized Banks in India

All of us are aware that in today’s fast – moving world, people can safely park their hard – earned money in banks and other financial institutions without worrying much about the safety of these invested funds.

Most of us are also reaping the maximum benefits of the banking schemes which are available presently in the nation.

These are the schemes which are forefathers were unheard of.

This was because, in the early years there was a lack of proper mechanism for people to safeguard their money and mostly all the individuals used to store and treasure their money in their homes.

On the 1st of April 2020, Finance Minister Nirmala Sitharaman announced the merger of 10 banks into 4.

This was done to stipulate the economic growth and reform the banking sector in India.

With effect of this massive amalgamation, the total number of the PSBs, popularly known as the Public Service Banks have reduced to 27 (2017) to 12 (2020).

Currently, there are twelve public sector banks in our country. All the banking operations in India are managed by the RBI, which is a central authority.

What is a Bank?

A financial institution that has been licensed for lending the loans to the needful and receiving the deposits.

Other than the mentioned, a bank performs many different functions such as safety deposit boxes, financial services, wealth management, currency exchange, etc.

However, the main function of a bank still remains lending money to different individuals and businesses and receiving deposits.

This process involves investing the fund securities, safeguarding payments, and disbursing them.

Categorization of Banking Sector

The banking sector is mainly characterized into 2 broad categories – namely, non – scheduled banks and scheduled banks.

The schedules banks are established and its operations are defined under the 2nd schedule of the RBI Act of 1934.

If we further categorize these banks, these banks can be divided into 5 main broad categories, which are –

  • Private Sector Banks
  • State Bank of India i.e. SBI and its associates
  • Regional Rural Banks
  • Foreign Banks
  • Nationalized Banks

What do you infer by Nationalized Banks?

When the public sector assets are owned or operated by the central or the state governments, then it is known as Nationalization.

The banks which were formerly operating under private sector were transferred to the public sector in our country.

This was done by the act of nationalization, and it was then that the nationalized banks came into existence.

The history of the banking sector in our country states that the GOI i.e. the Government of India initiated several different measures to play an important role in the economic development of India, after Independence.

This step led to the establishment of the RBI i.e. the Reserve Bank of India in the April of 1935.

Later, it was too nationalized during the year 1949 under the terms of RBI i.e. the Reserve Bank of India Act 1948.

The Government of India adopted a well – planned economic development for the betterment of India after independence.

The GOI i.e. the Government of India under the leadership of former honorable Prime Minister of India Smt. Indira Gandhi issued an ordinance.

This was done to nationalize fourteen largest commercial banks of the country.

This was done with effect from 19th of July, 1969 under the regulatory authority of the RBI.

You will be surprised to know that at the time of nationalization of these banks, these banks constitute up to approximately 85 per cent of the country’s deposits, and most of these were owned privately.

During the year of 1980, six more commercial banks followed the same suit and came under the same cover of nationalization.

However, the topic was in the light of the debate for a long period of time because till the 1990s, the growth of these nationalized banks grew at a snail’s pace of approximately 4 per cent annually.

Therefore, to address the problem stated above, the government of India adopted the liberalization policy during the early 1990s.

They licensed a very small no. of private banks in India that greatly helped for the rapid growth boom for the economic conditions of our country.

Reasons for the Nationalization of Banks in our country

  • For prioritizing the lending of the sector
  • For reducing the imbalance in the sector
  • For controlling the monopolies of the private sector
  • For expansion of the banking sector
  • For developing the habits of banking services
  • For social welfare

How many Nationalized Banks in India?

Public Sector Bank, or PSB is a bank which is either owned by the government or the government is the biggest shareholder of over fifty – one per cent in the bank.

Fun Fact – Under the SBI Act of 1955, SBI i.e. the State Bank of India became the 1st nationalized bank in our country.

As already stated, the Reserve Bank of India i.e. the RBI regulates the banking sector in the country.

The merger has been done with the aim of increasing the efficiency and productivity of the banks.

Alongside, the economy will be boosted as the number of NPAs and bad loans will be reduced.

The twelve public sector banks are –

1 Punjab National Bank

Headquarters – New Delhi

Tag Line –The Name you can surely bank upon

2 Indian Bank

Headquarters – Chennai

Tag Line – Your Tech – Friendly Bank

3 State Bank of India

Headquarters – Mumbai

Tag Line – The Nation’s Bank on Us

4 Canara Bank

Headquarters – Bangalore

Tag Line – Together we can

5 Indian Overseas Bank

Headquarters – Chennai

Tag Line – Good people to grow with

6 Union Bank of India

Headquarters – Mumbai

Tag Line – Good people to bank with

7 UCO Bank

Headquarters – Kolkata

Tag Line – Honors your Trust

8 Bank of Maharashtra

Headquarters – Pune

Tag Line – 1 Family, 1 Bank

9 Punjab and Sind Bank

Headquarters – Rajendra Place, New Delhi

Tag Line – Where Service is a way of Life

10 Bank of India

Headquarters – Mumbai

Tag Line – Relationships beyond banking

11 Central Bank of India

Headquarters – Mumbai

Tag Line – Build a better life around us

12 Bank of Baroda

Headquarters – Gujarat

Tag Line – India’s International Bank

Now, let’s discuss each of the banks one by one in detail.

1 Punjab National Bank

Punjab National Bank, also known as PNB was set up to help the Indian people.

It was the 1st Swadeshi Bank which is known to began its operations on 12th of April, 1895. It had the working capital of Rs. 20000 and the authorized capital of Rs. 2 lakhs.

2 Indian Bank

Along with the Swadeshi movement, a bank known as Indian Bank was born on 15th of August 1907.

It has all – India presence with as many as 9786 touch points. This included 3022 BCs + 3892 BNAs or ATMs + 2872 Domestic Branches.

3 State Bank of India

State Bank of India, which is popularly known as SBI has a history of more than 200 years.

It is by far the largest commercial bank of our nation.

It is also the largest bank of our nation in terms of employees, customers, branches, profits, deposits, and assets.

You will be surprised to know but the Government of India has over fifty per cent stake in State Bank of India i.e. the SBI.

It was formed when 3 banks – namely, the Bank of Madras, Bank of Calcutta, and the Bank of Bombay merged with each other.

It gave rise to the Imperial Bank of India. It was on 1st July, 1955 that the Imperial Bank of India was renamed as the State Bank of India.

It was when the GOI i.e. the Government of India acquired around 60 per cent of the stake in the Imperial Bank of India.

As already mentioned, the State Bank of India is by far the largest and the greatest bank if we determine or segregate by any factor.

It holds twenty – three per cent of the assets and a total of twenty – five per cent of the total deposits and loans markets.

Many people doesn’t think of the State Bank of India as a nationalized banks as its inception, it has been always a state – owned financial institution.

The SBI’s Net Sales Turnover stood at Rs. 2.2 lakh crore rupees for March 2018 against Rs. 1.75 lakh crore rupees during 2017 March, thus increasing by a significant twenty – three per cent.

4 Canara Bank

What we know as ‘Canara Bank‘ today was founded as ‘Canara Bank Hindu Permanent Fund’ in the year 1906.

It’s foundation was laid by the famous philanthropist of his times, Shri Ammembal Subba Rao Pai.

It was in the year 1910 that this small seed blossomed into a LMTD. Company which came to known as ‘Canara Bank Ltd.’.

After nationalization in the year 1969, it became the Canara Bank that we know today.

Canara Bank is one of the oldest PSBs i.e. Public Sector Banks in the nation.

Across several locations in our country, this bank has a chain of 10600 ATMs and 6639 branches.

As early as 1976 marks the year when the Canara Bank established an international division abroad for the very 1st time.

If we talk about the present conditions, the bank has its branches located and placed in many different locations in the world such as Moscow, Tanzania, Bahrain, Hong Kong, Shanghai, London, Doha, New York, Dubai, and South Africa.

During the fiscal year of 2018, the net worth of this bank was recorded at Rs. 5.24 lakh crore rupees against Rs. 4.95 lakh crores during the fiscal year of 2017, registering an increase of 5.97 per cent.

5 Union Bank of India

Union Bank of India was established in the year 1919, on the 11th of November.

The Union Bank of India now operates through more than 4200 + branches all over the country.

One quality that distinguishes this bank from the rest of the Public Sector Banks is that the Union Bank of India has shown uninterrupted profit during all the years of its operations.

This has been possible as the core values of this bank i.e. prudent management without ignoring opportunities has been followed all these years.

6 Indian Overseas Bank

Shri M.Ct.M Chidambaram Chettyar, who happens to be a pioneer in different fields, was the founding father of the Indian Overseas Bank.

He laid the foundation of this bank on the 10th of February 1937.

His main aim was to take the Indian Overseas Bank across the globe by specializing in the foreign exchange business in the banking sector.

7 UCO Bank

UCO Bank was founded in the year 1943 and is an advertisement bank.

It was a GOI i.e. the Government of India endeavour.

Its Board of administrators consists of depository financial institution of India and the state representatives from the Government of India.

It also includes professionals such as businessmen, economists, management consultants, and accountants, etc.

8 Bank of Maharashtra 

Maharashtra has always been a very progressive region of our nation.

We are stating this because the banking activity was started in this region quite early as compared to other regions in the country.

Traditionally speaking, this bank was established in the year 1840. It was primarily a depository financial type of institution in the geographic area.

However, there were formerly 2 depository financial institutions present –

  1. The Poona Bank in Pune which was established in the year 1889.
  2. It was followed by the establishment of the Deccan Bank in the year 1890.

Here’s an important information for all you folks and people who are prepping for government exams.

Q. Do you know which is the largest bank in India in terms of the number of branches all across the nation?

A. Yes, you got it right, mate – It  is none other than the Bank of Maharashtra.

As per the official figures, the Bank of Maharashtra has an estimated fifteen million customers worldwide.

D.K. Sathe and V.G. Kale laid the foundation of this bank in the year of 1935.

It was during the year of 1944 that this bank gained the status of a Scheduled Bank.

In the year 1998, the Bank of Maharashtra gained the autonomous status that helped in the reduction of the interference of the government’s bureaucracy in its decision – making process and the internal matters.

9 Punjab and Sind Bank

Punjab and Sind Bank was born with the idea for the upliftment of the poorest of the poor situated in the land.

Punjab and Sind Bank was established in the year 1908. Sardar Tarlochan Singh, Sir Sunder Singh Majitha and Bhai J Vir Singh planted the idea of the Punjab and Sind Bank.

This bank was established with the sole idea to help the weakest section of the society.

It was done to raise their standard of life by increasing their economic endeavours.

10 Bank of India

Eminent businessmen from Bombay (Now, Mumbai) established the Bank of India on the 7th of September, 1906.

Until July 1969, the Bank of India was beneath public management and possession.

The Bank of India rise over the year and blossomed into a very big establishment with sizable international operations and a robust national presence.

You will be surprised to know that this bank only had 50 workers and a paid capital of only Rs. 50 hundred thousand in the beginning, when it started its operations.

As of January 2017, the Bank of India had a total of 5100 branches all across the nation.

The headquarters of the Bank of India are located in Nariman Point, in Mumbai. 

11 Central Bank of India

Central Bank of India was established in the year 1911.

It was one of the 1st financial organizations of the country which was wholly managed and held by the responsible and educated citizens of our nation.

Sir Sorabji Pochkhanawala was the founding father of the Central Bank of India.

This bank was his dream and Sir Pherozesha Mehta was the primary chairman of the Central Bank of India.

12 Bank of Baroda

Bank of Baroda, which is a money services company and Indian state – owned international banking company is headquartered in the city of Vadodara in the state of Gujarat.

The city was formerly known as Baroda, thus the name of this bank i.e. The Bank of Baroda.

This bank was founded by the Prince of Baroda. His name was Prince Sayajirao Gaekwad III in the 20th Gregorian calendar month of the year 1908.

Therefore, this bank was founded during the era of pre – independence.

The Bank of Baroda is the 2nd – largest Indian bank if we talk about assets as the total assets of the bank are approximately Rs. 3.58 trillion rupees.

The corporate office of the Bank of Baroda is in Mumbai i.e. the financial capital of the nation.

Big Bank Mergers : Merger of ten Public Sector Banks (PSBs) into four

On 30th of August, 2019; the Government of India announced big bank mergers that took the nation by a big surprise.

This massive step was taken to improve the condition of a sector that is struggling with the clean-up of bad loans and NPAs, and create lenders of big scales in the world who can support the dipping economy of the country.

You might be aware that the Government of India and the honorable Prime Minister of India aspires to surge to 5 trillion dollars by the fiscal year of 2024.

Following the same policy, ten state – owned banks have been consolidated to four PSBs i.e. the Public Sector Banks.

This will help in the easy and quick realisation of the gains of the banks that are based on the use of the same Core Banking Solution (CBS) platform used in the bank.

The Honorable Finance Minister of the country, Smt. Nirmala Sitharaman stated that the government of India wants to create next – gen banks.

She said that the  country and the consumers need big banks that has enhanced capacity for increasing credit.

The country and the banking customers of the nation need banks with a strong global reach and national presence as well.

The Finance Minister also stressed upon the fact that it has been proposed that there will be 0 downsizing in the number of employees during and after the merger of these banks.

With this merger scheme, the total number of twenty – seven nationalized banks will be reduced to twelve PSU entities in the banking sector, post – merger.

The banks which are under the amalgamation according to the scheme of merger are –

United Bank, Oriental Bank of Commerce, and the Punjab National Bank

If we talk about this case, then Punjab National Bank, also known as PNB will be the anchor bank.

This amalgamation will be the 2nd – largest public sector bank (PSB) followed by the State Bank of India.

This merger will have a branching network of 11437 branches and a total business of Rs. 17.95 lakh crores rupees.

Syndicate Bank and Canara Bank

Both these banks are from South India and these will be merged with Canara bank being the anchor bank.

This will be the 4th – largest public sector bank (PSB) with a total business of Rs. 15.20 lakh crores rupees.

When these banks will be merged, the total combined network in these bank’s network will be of 10342 branches.

Corporation Bank, Andhra Bank and the Union Bank of India

This will be the 5th – largest public sector bank (PSB). The Union Bank of India will be the anchor bank in this case of merger.

The total consolidated business will be of Rs. 14.59 lakh crore rupees and the number of branches all over the nation will be 9609.

Allahabad Bank and Indian Bank

Allahabad Bank will be merged with the Indian Bank and the latter will be the anchor bank in this case of the merger of banks.

It will the 7th – largest public sector bank (PSB) after amalgamation.

Both these banks have a strong presence in Eastern, North, and Southern parts of our country; and the total consolidated business size of these banks will be Rs. 8.08 lakh crores rupees.

Categories gk

Single and Multidimensional Arrays in C++

Arrays is a kind of data structure that can store a elements of the same type.

Arrays stores the elements in a contiguous memory locations.

Array is a collection of variables of the same type.

 For example: we want to declare 100 integer variable

Instead of declaring 100 individual variables, such as

int number0, number1,number2,number3, … …., number99;

you declare one integer array variable such as

int number[100];

here number[100] is an integer array of size 100, means this array can store 100 integer value.

Array indexing starts from index 0 to n-1.

Means first integer number store in array number[0], second integer number store in array number[1], third integer number store in array number[2], and so on. Here 100th  number store in array numbers[99].

Array in Cpp
Array In C++

Declaring Arrays

Syntax of declare an array in C:

Data_type  arrayName [ arraySize ];

This is called a single-dimensional array.

The arraySize must be an integer constant greater than zero.

datatype can be any valid C data type.

Example: write a program to create integer array and store 5 integer number and print.  


Array example in cpp
Element Position in Array

Description : In the above program,   “int  num[5]” array can store 5 element in a contiguous memory locations from index 0 to 4.         

Example: Write a program to take 5 number from user and store integer number in integer array and print.  


Another way to Initialize Array

 We can also initialize array by this way:

int num[5] = { 54 , 4,  13,  2,  17 };

above integer array num[5], stores five numbers. Where num[0] store 54, num[1] store 4….. , & num[4] store 17.


Multi-dimensional Arrays 

C++  programming language also support multidimensional arrays.

Syntax of multidimensional array declaration:

Data_type    array_name[size1][size2]…[sizeN];

For example, if we want to creates a three dimensional integer array −

int  num[5][10][4];

Two-dimensional Arrays

The simplest form of multidimensional array is the two-dimensional array.

If we want to declare a two dimensional integer array of size [x][y] ( where x is a  number of rows and y is a number of columns ) you would write something as follows −

 int  a[3][4];

In the above line we have created two-dimensional integer array ”a” with 3 rows and 4 columns.

Two Dimensional (2d array)
Two Dimensional Array

Initializing Two-Dimensional Arrays

In a C++ programming language  Multidimensional arrays may be initialized by specifying bracketed values for each row.

For example we want to create an integer array with 3 rows and each row has 4 columns.

Above the nested braces indicates the intended rows.

The following initialization is equivalent to the previous example −

int a[3][4] = {1,2,3,4,5,6,7,8,9,10,11,12};

which indicate the nested braces for intended row, are optional.

Accessing Elements of Two-Dimensional Array

Create a two-dimensional array of size 3*3 and  nested loop is used to handle a two-dimensional array −


Example: Write a program to create 3*3 matrix and take all elements of matrix as an input from user and print it.


CPP Program to Addition of Two Matrix

Example: Write a program to add two matrix.

Firstly, ask from user order of matrix( number of rows and column).

Then take the elements of matrix from user as a input and print the resultant matrix

For example, if a user input order as 3,3, i.e., three rows and three columns and

First matrix                      

1          2          3
4          5          6
7          8          9

Second matrix:

9          8          7
6          5          4
3          2          1

then the output of the program (addition of the two matrices) is:

10        10        10
10        10        10
10        10        10

Matrix addition program in C++


Matrix multiplication in CPP

Example: Write a program to multiply two matrix. Firstly, ask from user order of matrix( number of rows and column). Then take the elements of matrix from user as a input and print the resultant matrix.

Read More

Functions in C++

Categories C++