How Open Source Changed The Face Of Businesses And Programming


Everyone in this world is using open source platform in one way or the other, irrespective of whether they realize it or not. Ever since its inception, open source has been a huge hit among software developers as well as businesses. February 2018 marked the 20th Anniversary of the Open Source Initiative (OSI), a movement that was started way back in 1998 by Eric Raymond and Bruce Perens. The aim was to make the source code accessible to all so that developers around the world can tweak and make useful additions to a software to enrich it even further.

Essentially, the open source community is comprised of both professional and amateur developers who enhance, modify, and work on publicly available source code. And businesses can utilize the code through tools such as Github, to build their own products and services. Today, more and more businesses around the world are joining the Open source bandwagon as it helps them save both time and money.

Over the years, the open source platform has made a considerable impact on programming and businesses. In an annual survey conducted Black Duck, it was found that of the 1,300 respondents, nearly 78% agreed to run their business operations (at least partly) on open source software. Here’s how open source is helping enrich programming and businesses:

  1. Viable And Accessible

During the initial years of the OSI, Linux largely remained beyond the understanding of man. It was not very user-friendly, as in, it was pretty difficult to install and work on. Today, however, the situation has changed a lot. After decades of modifications and enhancements, Linux is now much more convenient and easy to use. In fact, the popularity of Linux has escalated so high that the top 500 supercomputers of the world run on Linux.

  1. The ‘Source’ Behind Android’s Power!

Yes, open source is what makes Android so easy to work with. It is based on Linux. Many companies in the market today are leveraging open source software such as Linux or Tizen to build their Android platform. However, while most Android phones are based on open source OS, the end product is a closed source one, that is, it cannot be tweaked or modified.

  1. Run The Cloud!

The popularity of open source has caught on so fast that more and more people are now inclining towards services/products that can be accessed through the Web instead of using local apps for the same. Basically, the websites are loaded on someone’s computer somewhere in the world and open source platform allows users to access such websites. FreeBSD and Linux are a hit for cloud servers that allow others to access web services remotely.

  1. The Web – Made Easy

Today, anyone can have their own website, all thanks to open source. Take WordPress, for instance. WordPress is the backend for many websites – while obtaining a domain name is going to cost you, building a website is essentially free. It is this open source feature that has made WordPress a renowned and trusted name all over the world.

  1. The Baton Continues To Progress Beyond The ‘Code’

Open source has gained such as strong foothold that companies are no longer just using it but are also helping progress it by launching their own open source codes. Reputed names such as Walmart and ExxonMobil are following suit. While Walmart launched an open source cloud management system, ExxonMobil released an open source developer toolkit. Furthermore, magnates of the finance world such as JP Morgan and London Stock Exchange Group have adopted Hyperledger, an open source software that holds the potential to completely transform the share market scenario.

Open source is massive. Practically every software that is a big hit with us today is rooted in open source. Jim Whitehurst, CEO of Red Hat, maintains:

“The future of open source is bright. We are on the cusp of a new wave of innovation that will come about because information is being separated from physical objects thanks to the Internet of Things. Over the next decade, we will see entire industries based on open-source concepts, like the sharing of information and joint innovation, become mainstream.”

So, folks, this is only the beginning for there’s a lot more to come!

Pros and Cons of open source programming languages


We’ve all heard about open source software and open source programming languages at some point in our lives. However, do you know what the term actually means?

“Open Source” basically refers to a platform or product that can be accessed by anyone and everyone. Open-source allows you to access, analyze, and tweak the source code for the purpose of enhancing it. Similarly, an open-source programming language is one that falls within the open-source protocol, that is, it can be accessed and modified by the public. Today, programmers have an array of options when it comes to open-source programming languages. For a programming language to be considered as open-source, it has to fulfill the following criteria:

  • The source code must be accessible by all.
  • The integrity of the source code must be maintained.
  • It should allow free distribution and redistribution.
  • It should allow free distribution of license.
  • Its license should be technology-neutral and should not restrict other software.
  • It should not discriminate between persons/groups and also against any field of endeavor.

Now that we know what an open-source programming language is, let us look at some of the most widely used open-source languages as well as the advantages and disadvantages each brings.

  1. Java

Java is undoubtedly one of the most popular open-source programming languages among programmers. It is a highly class-based and featured object-oriented language. Today, Java forms the core of various web and mobile applications across a wide range of operating systems and devices.


  • Easy to write, compile, and debug.
  • Excellent for developing modular programs and reusable code.
  • It is platform independent, both at the binary and source levels.


  • It is not speed-intensive and hence, can be very time-consuming.
  • It is an interpreted language.
  • Lacks support for low-level programming languages.
  1. Python

Python is a versatile programming language that anyone can master with a little dedication and effort. It was developed by  Guido van Rossum back in the 1980s and is controlled and managed by the Python Software Foundation. It is used by developers around the world as a scripting language allowing them to generate readable and functional code in a short span of time.


  • It comes with readable and simple syntax.
  • It can be interpreted in many operating systems owing to its portability, such as Unix, Windows, and Mac OS, to name a few.
  • It supports both object-oriented and procedure-oriented programming.


  • Lacks pre-packaged solutions.
  • Lacks adequate GUI tools.
  • Not ideal for memory intensive functions.
  1. Ruby

Developed by  Yukihiro Matsumoto in the 90’s, Ruby is an object-oriented programming language that has gained huge traction over the past few years. The best thing about Ruby is its neat, clear, and simple syntax that makes it the ideal programming language for beginners. Today, Ruby along with the Rails framework is being used by reputed names such as Airbnb, GitHub, Basecamp, and Bloomberg.


  • It facilitates easy inheritance of features from standard classes.
  • Offers a clean object hierarchy thereby making metaprogramming very convenient.
  • Comes with a provision of open classes.
  • It allows you to write codes from any line or column.


  • Backed by a relatively small community.
  • Processing can often be quite slow.
  • Lacks regular updates.
  1. PHP

Over the years, PHP has emerged as one of the most developer-friendly open-source programming languages. It is much faster than many other programming languages, and that’s primarily why PHP-based solutions are today being used by developers, entrepreneurs, and SMEs around the globe.


  • Offers cross-platform compatibility. Can run on platforms such as Linux, Windows, Mac OS, and Unix.
  • Comes with pre-defined error reporting constants.
  • Allows the generation of dynamic page content.
  • Is a loosely typed language so you don’t need to explicitly declare the data type of variables.


  • Lacks named parameters.
  • Not ideal for very large projects since it is less modular.
  • The language semantics can only be altered by global configuration parameters that can pose complications during deployment.
  1. Perl

Perl is a mature, stable, and highly feature-rich programming language. It is an excellent choice for scripting and processing large projects. Also, it is backed by a wide community of developers. It runs successfully over a hundred platforms!


  • Excellent file manipulation.
  • Supports cross-platform usage. Thus, codes written for a specific platform can also be run on other platforms with minimal or no modifications.
  • Comes with a library of modules.


  • Can be slower for specific tasks such as scripting.
  • Poor argument handling.
  • Poor usability factor.

To conclude, each language has its distinct benefits and disadvantages. Depending on the kind of project you will be working on, you can choose the open-source programming language that best suits your project requirements.

Happy coding!

How To Prepare For ACM-ICPC?


Considered as the “Olympics of Programming Competitions,” the Association for Computing Machinery – International Collegiate Programming Contest or ACM-ICPC is one of the oldest and most esteemed programming competitions in the world. Every year, more than 2,000 universities spread across eighty countries participate in this contest to win the grand prize of $12,000 and a gold medal awarded by the ACM-ICPC. Apart from this, three runner-up teams receiving the Gold medal are also rewarded with $6,000 and teams that bag the Silver and Bronze medal receive $3,000 and $1,500 respectively.

The ACM-ICPC contest has two rounds, the regional round that is organized and conducted at the local universities of various regions across the world. In India, the Asia Regionals are held at Amritapuri, Chennai, and Kolkata. The regional contest location sites are allotted a ‘slot’ which is the formal invitation to enter the ACM-ICPC World Finals.

Here’s how you should approach the ACM-ICPC preparation!

ACM-ICPC Preparation

The first step in preparing for the ACM-ICPC is to start practicing and competing in Online Judges and contests similar to ICPC. OJs such as CodeChef, TopCoder, Codeforces, Hackerrank, Hackerearth, USACO are excellent online resources to practice and hone your programming skills. Begin by tracking down problems with the most number of submissions, see how people have approached it, and see if you can get to the solution through a different and better perspective.

The next step would be to make a detailed list of the things you have to study. Having a computer science background is highly preferred as you’ll have to deal with a lot of complex data structures and algorithms in the contest.

Programming Language

There are many programming languages to choose from including C++, Java, Ruby, Python, Perl, and so on. Pick one programming language that you think will be the best match for you and master it through and through. Once you master a programming language, you’ll find it much easier to learn other programming languages.

Data Structures

Mastering data structures is one of the prerequisites for participating in any competitive programming contest. Thus, you are expected to be well-versed with the fundamental data structures such as arrays, stacks, queues, strings, heap, and hash, to name a few. Once you’ve got a solid grasp on these basic data structures, it’s time to move on to more advanced data structures like Fenwick Tree, K-D Tree, Segment Tree, Interval Tree, and so on.

Sorting & Searching

After data structures, you should focus on learning sorting and searching functions like quick sort, merge sort, binary search, and order statistics. While learning the basic concepts of sorting and searching functions are fine, you must also familiarize yourself with as many library functions as possible.


Once you learn how to work with strings, you’ll find it extremely interesting. Strings are a must-learn as they are highly used in competitive programming contests. Try to learn how to manipulate string functions such as Z’s algorithm, KMP algorithm, Rabin Karp, and Aho Corasick string matching, to name a few. If you can learn library functions for strings, even better!


Just like data structures, algorithms are also very important for competitive programming. Algorithms can be classified under the following categories:

Dynamic Programming algorithms – Longest Common Subsequence, Longest Increasing Subsequence, Minimum Partition, Longest Path In Matrix, Subset Sum Problem, 0-1 Knapsack Problem, and Assembly Line Scheduling.

  • Graph algorithms – Breadth First Search (BFS), Depth First Search (DFS), Dijkstra, Floyd Warshall, Prim, Kruskal, Johnson’s algorithm, Topological Sort, and Bridges in a graph.
  • Greedy algorithms – Activity Selection Problem, Huffman Coding, Huffman Decoding, Egyptian Fraction, Job Sequencing Problem, Water Connection Problem, and Fitting Shelves Problem.
  • Geometric algorithms – Convex Hull, Graham Scan, Line Intersection, Matrix Exponentiation, Bentley Ottmann algorithm, Rotating Calipers Technique, Closest pair of points, and Voronoi Diagrams of n points using Fortune’s algorithm.
  • Network Flow algorithms – Maxflow Ford Fulkerson algorithm, Edmond Karp Implementation, Min-cut, Stable Marriage Problem, Dinic’s Algorithm, Cycle Cancelling algorithm, Stoer Wagner min-cut algorithm, and Hopcroft–Karp Algorithm.


When it comes to programming, one must have a solid foundation in Mathematics and Statistics since a lot of programming problems are rooted in these two. Thus, you must learn basic math concepts such as number theory, linear algebra, discrete mathematics and combinatorics, probability theory, game theory, graph theory, numerical analysis, and calculus. Also, be well-versed with principles like Induction, Pigeon Hole, Inclusion-Exclusion, and the like. Moving on to Statistics, make sure that you learn concepts like mean, median, variance, and so on.

Mastering the above-mentioned concepts should be enough for you to make it through the regional round of the ACM-ICPC, and once that’s done, there’s nothing to stop you from emerging as a winner in this prestigious competition. The key to success is dedication, commitment, and practice. During the learning process, try to connect with mentors and peers on the various OJ platforms. Usually, such communities are very active and can help you with helpful feedback if you are ever stuck somewhere. Be confident, and begin!

Till then, happy coding!

Getting Started With And Mastering The Basics Of Data Structures


Data Structures is one of the foundation courses in Computer Science. Essentially, it includes concepts such as stacks, queues, graphs, algorithm analysis, sorting algorithms, linked lists, and set implementations (binary search trees and hash tables). While it is true that data structures and algorithms are difficult to master, it is not an impossible feat. The key to learning these tricky concepts is to follow the right approach, and that’s what we’re going to show you in this post.

  1. Strengthen Your Fundamentals

The foremost requirement for mastering data structures and algorithms is a solid foundation of the fundamental concepts of Mathematics, Computer Architecture, and of course Data Structures.  

  • Maths – A concrete mathematical base is essential to learn algorithms. Try to cover Math concepts from different areas such as set theory, regular expressions, linear equations, finite-state machines, matrix multiplication, and basic combinatorial principles like pigeonhole, permutations, and combinations.
  • Primary Data Structures – Strengthen your knowledge about the primary data structure concepts like arrays, hash tables, graphs, stacks, queues, heaps, linked lists, and binary trees.
  • Computer Architecture – To be able to work with data structures and algorithms you need to have basic knowledge of computer arithmetic, boolean algebra, floating-point representation, cache design, and digital logic design. If you are well-versed in programming languages such as C, C++, Python, Java, and Ruby, you’ll have the edge over your competitors.
  1. Learn To Visualize Data Structures

To become a great coder, one must possess the ability to visualize different data structures. You need to picture how a data structure will look like, how you can implement it within your code, and also how it is organized in your computer ’s memory as well as in the abstract. To visualize data structures, you can pen down your thought pattern on paper. As you gradually master visualization, you’ll find it much easier to solve all kinds of problems, be it simple ones like stacks or be it self-balancing trees.

  1. Dive Into Algorithms

Once you’ve learned about the fundamental concepts of data structures, it’s time to leap into algorithms. An excellent way to start would be to learn Big-O notation as it would help you understand how to classify algorithms based on their running time and space requirements. Books like Introduction to Algorithms, and Algorithm Design Manual are great learning options.

As you learn more about algorithms, you should start implementing algorithms in your codes and learn about their running times in real-time. You could try implementing Euclid’s algorithm, Binary search, Binary tree traversals, Dijkstra’s shortest path, Min & max heaps, to name a few. Platforms such as HackerRank, LeetCode, CodeChef, and  Coderbyte are excellent for practicing coding and sharpen your coding skills.

  1. Learn Dynamic Programming

If you wish to master data structures and algorithms, dynamic programming is a must. It is a technique of solving complicated problems by breaking them down into smaller fragments of “subproblems.” These subproblems are then solved at once and their solutions are stored for future reference. Thus, in future if similar problems occur, you don’t need to solve it again from scratch; instead, you can refer to the previous solutions and save both time and effort.

So, there, you have it – your stepwise guide to approaching and mastering data structures and algorithms. If you follow these four steps diligently and with dedicated passion, you can surely master data structures in a matter of few months. And, if you feel stuck anywhere along your journey, don’t forget to check us out at,

Happy learning!

The OOPS Concepts You Must Know


OOPS, or Object Oriented Programming System is one of the most popular programming architectures even today. It is used by software developers around the world owing to the many, many advantages that it offers. However, to be able to use OOPS to its full potential you need to have a fair understanding of its fundamental concepts.

So, we’ve prepared this informative guide to introduce you to some of the basic concepts of OOPS.

  1. Class

A class denotes a blueprint containing elements having certain common traits, that is, they perform the same functions, or they possess the same features. In other words, a class represents data with variables known as ‘fields’ and behaviors with functions known as ‘methods.’

In a class, the members (functions and variables) may either have productive behaviors or may be intended to perform specific actions along with member attributes. The members of a class can only be accessed by the corresponding class or its objects.  

  1. Object

While class denotes that model or prototype, an object is the actual player of a class, that is, an object is an instance of a class. Precisely, objects are variables of a user-defined data type class. The variables of a class contain the data and the functions perform specific operations based on the objects contained in a class.

  1. Inheritance

Often, solving complex programming problems requires you to build classes that possess certain common characteristics or behaviors. Thus, to avoid repetition you need to gather all the common characteristics within a single class known as the ‘parent class.’ Now, all the classes having similar features can ‘inherit’ the common traits from the parent class. This is called ‘Inheritance.’ Apart from inherited features from the parent class, the new classes can have their distinct fields and methods.

There also exists the concept of ‘multiple inheritances’ wherein some object-oreinted programming languages support the inheritance of specific features from multiple classes.

  1. Abstraction

Abstraction refers to the process of eliminating complexity by hiding the unnecessary details of the implementation from the user. This allows the user to apply a more complex logic on top of the given abstraction without even being aware of the underlying or hidden complexity.

  1. Encapsulation

Encapsulation refers to the process of bundling methods and data that function on the data contained within a single unit such as a class. There are two aspects of encapsulation – one is the restrict the access to particular fields and methods of a class and second is the merge similar data and methods.

It is often used to hide the internal representation of an object from the user which is known as ‘information hiding.’

  1. Interface

An interface is a programming protocol for two unrelated classes to communicate with one another. An alternative to inheritance, an interface defines the actions of objects. An interface is placed within its distinct file bearing the same name as the interface (in Capital) and ends with the familiar language extension, for instance, “.as”. The primary purpose of interfaces is to enable the computer to know what kind of properties a particular object should have and to enforce those properties on the object.

  1. Association

An association denotes any type of relationship between two objects. Precisely, it refers to the multiple associations between objects such as one-to-one, one-to-many, many-to-one, and many-to-many. The objects may be instances of unrelated classes and they collaborate to accomplish their unique goals without manipulating the lifecycle of the other.

  1. Aggregation

Aggregation is nothing but a special type of association. It is a relationship where one object belongs to another object but despite that, they are both independent objects. It is also known as a “has-a” relationship.

  1. Composition

A composition is a relationship between two objects where one object is under complete control of the other, and hence, has no independent lifecycle. While it is similar to aggregation as in one object contains another object, unlike aggregation, the object contained cannot exist without the container object.  

  1. Polymorphism

In general terms ‘polymorphism’ refers to the ability to exist in multiple forms. However, in OOPS, the term polymorphism refers to the ability of a programming language to process objects differently in accordance with their data type or class. Thus, polymorphism is essentially a language’s ability to ‘redefine’ the methods for different derived classes.

While this is just a nudge to steer you in the right direction, there are many other concepts that you must learn to master OOPS. For now, starting with the basics would be the wise way to go. If guidance is what you seek, drop by at Coding Ninjas and we’ll get you covered with our wide array of courses in and around the most-used programming languages.

Competitive programming helps in bagging a better job. Here’s how!

FireShot Capture 095 - Competitive programming helps in bagg_ -

Competitive programming has become a worldwide trend among software developers and coders. It is both thrilling and challenging at the same time – the pressure of solving complex coding problems within a limited time and the thrill of being able to perform well! Today, competitive programming sites like TopCoder, Coderbyte, HackerRank, CodeChef, and so on are becoming hugely popular as more and more people are joining in to compete in their coding competitions.


While participating in coding competitions is not an absolute necessity, you should definitely give it a try. Why, you ask? Competitive programming has certain advantages that can help prepare you for coding interviews or better yet, to bag that prestigious job position in the company you’ve been dying to work for!

Let’s look at how competitive programming can help you become a better coder and also bag better jobs in the industry.

Makes You Faster And More Focused

The very essence of competitive programming is to be able to solve coding problems within an allotted time period. The more you participate in such competitions, the better and faster you get at solving problems quickly. Appearing in these contests regularly will enhance your coding skills and speed in solving problems. You will learn how to stay focused on the task because if you lose focus, you lose time, and ultimately, you lose the contest.

Makes You A Team Player

In competitive programming contests, you often have to work with other people on your team and come up with efficient solutions for specific tasks. As you start working with other people, you understand your core strengths and weaknesses as well as those of your team members too. You learn how to stay organized and well-coordinated even during stressful situations.

These qualities are what most companies look for in a potential candidate. However, one of the most compelling reasons to pursue competitive programming is that it provides you exposure.

You Are Being Watched!

Yes, when you enter the world of competitive programming, you are being observed by potential employers. Competitive programming is nothing short of a hunting ground for IT and Tech companies who are always on the lookout for talented, experienced, and well-learned software professionals. In fact, some of these contests are closely followed by industry magnates such as Apple, Google, and Facebook. So, if you are a dedicated and aspiring coder, participating in competitive programming challenges might someday turn out to be a big win for you. If you’re excellent at it, you might catch the attention of a reputed software company or an MNC.

Coding competitions are great for building the right foundation for cracking coding interviews. This is primarily because:

  • Both, coding contests and interviews will require you to have a good knowledge of computer science and data science concepts.
  • During both, you’ll have to write codes and find solutions to problems while adhering to the standard coding norms.

In coding interviews, however, you’ll not only be judged by the deftness of your problem-solving skills but also various other soft skills such as communication skills, team skills, cultural fit, and so on. Thus while, coding competitions are excellent for preparing yourself for the rigorous interview rounds, it alone does not guarantee that you’ll bag a job at reputed firms or companies. The greatest advantage of having a competitive coding background is that it’ll not only help you break the ice during coding interviews but it will also provide you an edge over your competitors.

Many success stories of competitive coders have come to the fore of how they bagged jobs at prestigious companies while leaving some of the top candidates with higher ranking CGPA behind them. For instance, Anudeep Nekkanti from Visakhapatnam bagged a job at Google. Passionate about coding, Anudeep states:


“My failure at IOPC 2012 made me start it. I thought I will do well in IOPC 2013 and stop programming. That is how I started it. Very soon I started to like it…My heart beat raises whenever I submit a solution. I get goosebumps. It was that fun that kept me going. Don’t do it, Play it. Enjoy it…Right now I am preparing for world finals. I am doing problems from various online judges like Topcoder, Codechef, Codeforces.”


So, that’s what we wanted to get across to you – start competitive coding and who knows you too might end up bagging a great job at an MNC just like Anudeep!

Most Commonly Asked ML Interview Questions


While Data is the new currency of the business and the industrial world, Data Science is the pathway to the next Industrial Revolution. The rising importance of data is creating a demand for skilled professionals who are well-versed in data science technologies such as Machine Learning (ML) and Artificial Intelligence (AI). However, bagging a job in the field isn’t a walk in the park. You need to be prepared to face the challenging interview process during which your mastery over a variety of data skills will be assessed such as your basic knowledge about data science and ML concepts; your ability to analyze and visualize data; your technical and programming skills, and so on.

We know that interviews can be tricky and overwhelming, and hence, we’ve prepared a list of ten most commonly asked machine learning interviews questions.

  1. What is the difference between supervised and unsupervised machine learning?

The primary difference between supervised and unsupervised learning is that while supervised learning focuses on training labeled data unsupervised learning does not require the data to be trained explicitly. For instance, for the classification function of supervised learning, one first needs to label the data that will be used to train the data model to classify the data into labeled subsets. This kind of specialized training is not required in unsupervised learning.

  1. What are parametric and non-parametric models?

Parametric models refer to those models that contain a finite number of parameters. In such a model, one only needs to know the parameters of the model to be able to predict new data. Linear regression, logistic regression, Naive Bayes, and Perceptron, are some examples of parametric models. Non-parametric models, on the other hand, contain an unlimited number of parameters and hence, are more flexible. In this model, apart from knowing the parameters of the model you also need to be aware of the state of the observed data. Decision trees, SVMs, and k-nearest neighbors are examples of non-parametric models.

  1. Explain the bias-variance tradeoff.

Predictive models usually have a tradeoff between bias and variance. While bias refers to the error occurring due to erroneous or overly simplistic assumptions in the learning algorithm being used, the variance is the error occurring due to excessively complicated assumptions in the learning algorithm in question. The purpose of the bias-variance is to  minimize the learning error of a specific algorithm by adding the bias and the variance along with some other irreducible errors due that originate from the noise in underlying datasets. For instance, you can reduce the bias by adding more variables to the model to make it complex, but in the process, you’ll add some variance to the model. Thus, to strike a perfect balance in the model, you need to have a tradeoff between bias and variance.

  1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

Both SGD and GD algorithms are techniques of finding a set of parameters that can reduce the loss function of a model. The parameters are first evaluated against the data, and then adjustments are made accordingly. However, there lies a subtle difference in the approach of the two algorithms. While in GD one needs to evaluate all the training samples for each set of parameters, in SGD you need to evaluate only one training sample for the given set of parameters. Also, GD is ideal for small datasets while SGD is ideal for more massive datasets.

  1. What is the purpose of the Box-Cox transformation?

The Box-Cox transformation is a standard power transformation process of transforming datasets to facilitate normal distribution. In other words, it is used to stabilize the variance in datasets. Since most well-known statistical methods sync well with normally distributed data, it is wise to normalize the distribution using this method.

  1. Why is Naive Bayes ‘naive’?

Naive Bayes is considered to be ‘naive’ mainly because it makes such assumptions that are nearly impossible to observe in real-life data. This algorithm assumes that the presence or absence of a particular feature of a class is unrelated to the presence or absence of any other feature of the class variable in question. This entails the “absolute independence of features,” a condition that can never be fulfilled in reality.

  1. What is the difference between machine learning and deep learning?

Deep learning is a branch of machine learning exclusively concerned with neural networks. It focuses on the ways to leverage certain principles of neuroscience to model large sets of unstructured or semi-structured data with increased accuracy. To be precise, deep learning is much like an unsupervised learning algorithm that aims to ‘learn’ data representations by leveraging neural nets.

  1. How will you choose a classifier based on a training set?

In case the training set is a small, models with high bias/variance, for example, Naive Bayes, are the best fit since they are less likely to overfit. Whereas if the training set is too large, models with low bias/variance such as logistic regression are best as they can detect more complex relations in data models.

  1. What is Latent Dirichlet Allocation (LDA)?

Latent Dirichlet Allocation (LDA) is a generative model that represents documents as an amalgamation of topics, each of which has their distinct probability distribution of possible words. In other words, LDA is a technique of classifying topics or documents according to the subject matter.

  1. What is the ROC curve? What is AUROC?

The ROC (Receiving Operating Characteristic) curve is a graphical representation of the contrast between true positive rates and the false positive rate at varying thresholds. It’s mostly used to assess the sensitivity of the true positives against the false positives’ probability to trigger a false alarm.

AUROC (Area Under the Receiving Operating Characteristic) denotes a standard performance metric used to evaluate binary classification models.

Are you eager to master machine learning? Do you wish to begin a career in machine learning? Come, join our Machine Learning course at Coding Ninjas today!

Five Skills You Need To Become An ML Engineer


Today, cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are bringing in the age of Technological Revolution. These technologies are helping shape not just our present but also laying the foundations for a better tomorrow. Companies across the world are now investing in these technologies to foster innovation, thus creating the demand for skilled and talented data professionals.

Machine Learning is a branch of Artificial Intelligence that enables machines to perform advanced tasks like prediction, image/voice recognition, robotics control, and so on without being exclusively programmed for those tasks. ML technology is mainly algorithm-oriented, that is, it aims to create such algorithms as can ’learn’ from the behavior of data and adapt accordingly to produce the best possible outcomes. In this respect, ML is somewhat similar to data mining – both of these fields are interested in sifting through data to uncover valuable insights and patterns hidden within data. However, ML has the edge over data mining.

While aspiring to begin a career in Machine Learning is undoubtedly commendable, you must first develop the requisite skills that are demanded of machine learning specialists. Here are the five skills that every machine learning engineer should develop.

  1. Fundamental Concepts of Computer Science and Programming

If you wish to be an ML engineer, you need to have a strong background in Computer Science. You need to be well-versed with core concepts of Computer Science such as data structures (trees, graphs, stacks, multi-dimensional arrays, etc.), algorithms, computability and complexity concepts such as big-O notation, approximate algorithms, NP-complete problems, and much more. Then again, you need to have a flair for high-level programming languages such as C++, Java, R, Ruby, JavaScript, Python, Perl, etc.

  1. Probability and Statistics

Machine Learning is closely associated with Probability and Statistics. In fact, probability concepts such as conditional probability, Naive Bayes rule, Markov Decision Processes, Hidden Markov Models, etc., make up the core of various ML algorithms. Statistics, on the other hand, provides the foundation for distributions, measures, and analysis techniques that are required for building and analyzing data models.

  1. Data Modeling and Evaluation

Data Modelling is the technique of estimating the implicit structure of a particular dataset to extract meaningful information from the data and to predict hidden trends from within. In this process, you have to evaluate the relevance and efficacy of the data model continually and to do this you must choose such an accuracy measure and evaluation strategy that best fits the data model in question.

  1. ML Libraries

To be able to implement ML algorithms appropriately, you need to be familiar with ML libraries or APIs such as Theano, Spark, TensorFlow, and Scikit-learn, to name a few. However, for these APIs to function optimally, you need to have extensive knowledge about classification algorithms like decision trees, neural nets, support vector machines, etc. The more you practice with these algorithms and APIs, the better will be your grasp on machine learning. You can try ML challenges on platforms such as Kaggle that will introduce you to a vast range of complex algorithmic problems.

  1. Software Engineering and System Design

An ML engineer is a software engineer and machine learning expert rolled into one. Essentially, an ML engineer’s core task is to build innovative software. To develop a software, you need to learn how to integrate smaller fragments to create the bigger picture. Thus, the software you create should be compatible with your system design. It should be scalable, flexible, and efficient so that it can adapt according to the ever-mounting volumes of data.

While mastering these major skills is a must for building a career in Machine Learning, you must also never forget that it is still a developing field. Thus, it is important that you constantly update yourself with the latest trends and innovations in ML and upskill accordingly.

If you are interested in building a career in Machine Learning, our Machine Learning course – Cognizance is what you need. At Coding Ninjas, our expert and qualified panel of instructors will ensure that by the end of the course, you are fully aware of the nitty-gritty of machine learning.

Basic C++ Interview Questions


C++ is general-purpose, object-oriented programming (OOP) language that was developed by Bjarne Stroustrup. An extension of the C programming language, C++ combines the features of both low-level and high-level languages and is hence considered to be a hybrid, intermediate level programming language.

Even today, C++ is considered to be a requisite skill for CS students and engineers. It is one of the most challenging programming languages since when writing programs with C++, one has to do extensive thinking. Hence, to crack a C++ interview successfully, you must be well prepared.

To help you crack your next C++ interview, we’ve created a list of some of the most commonly asked questions during interviews.

  1. Name the basic components of OOP language?

The essential components of an object-oriented programming language are:

  • Objects
  • Classes
  • Inheritance
  • Polymorphism
  • Message passing
  • Dynamic Binding
  • Data abstraction and encapsulation
  1. How is C++ different from C?

Since C++ is an extension of C, most programs written in C are compatible with C++ too. The primary differences between the two languages are:

  • While C is a procedural programming language, C++ supports both procedural and OOP features.
  • As C++ is an OOP language, it supports features like inheritance, templates, classes, function overloading, and virtual functions, to name a few. C programming language is devoid of these functions.
  • While in C exception handling is done in traditional ‘if-else’ technique, in C++ exception handling is accomplished at the language level.
  • In C++ input and output operations are performed through streams (cin and cout), whereas in C, scanf() and printf() are used for input and output functions.
  1. What is inheritance?

Inheritance is the technique of deriving a new class known as ‘derived’ from an old class known as ‘base class.’ With the help of this process, you don’t need to write a code from scratch; you can reuse an existing code by modifying it.

  1. What are virtual functions?

Virtual functions are integrated with inheritance to ensure that the correct function has been deployed to point to a particular object. Instead of naming them according to the type of reference or pointer, virtual functions are named according to the kind of object being referred to. The functions are named with a virtual keyword in the base class.

  1. What are the C++ access specifiers?

Access specifiers define how functions and variables (members) can be accessed outside of a given class. They can be classified into three types:

  • Private: The functions and variables that are labeled as ‘private’ can be accessed only within the same class and not outside their declared class.
  • Public: When members are labeled as ‘public,’ they can be accessed from anywhere.
  • Protected: Protected members can be accessed only by the declared class its child classes.
  1. Define Friend class and Friend function in C++.

A friend class can access both private and protected members of other classes in which it is specified as a friend. For instance, a LinkedList class may be declared as a friend of Node to access the private members of Node.

Like a Friend class, a Friend function can be granted access to private and protected members of other classes. A friend function can either be a method of another class or a global function. It should be kept in mind that Friend classes and functions should be used only for specific purposes to prevent the drop in the encapsulation value of different classes in OOP.

  1. What is s Static Member?

Static Member is a keyword in C++ that is used to bestow unique characteristics to a specific element. Static members cannot be virtual, and they also do not possess ‘this’ pointer. Static elements are allotted a storage space in the static storage area. While they need to be stored only once in a program lifetime, their life scope equals the program lifetime.

  1. What is a default constructor?

Default constructors are those that do not accept any parameters. For instance, if a default constructor is needed in a particular class (say, class A) where there exists no user-defined constructor, the compiler will implicitly declare a default constructor without any parameter – A::A(). This constructor is devoid of any constructor initializer and a null body.

  1. What are tokens?

Tokens are the smallest individual units of a program written in C++. They are as follows:

  • Identifiers
  • Constants
  • Keywords
  • Strings
  • Operators
  1. What is an iterator class?

Iterator class allows you to access classes that are inside containers that hold data structures, classes, and abstract data types. Iterators are crucial to understanding the functioning of C++ Standard Template Library (STL) since it offers a way to access data stored in the container classes, for example, maps, list, vector, and so on.

Want to get an in-depth understanding of C++ and its underlying concepts? Join Coding Ninjas today! Our trained and dedicated team of instructors will ensure that by the time you finish the course, you’ve mastered the art of C++.

7 Rookie Mistakes Every Android Developer Should Avoid


Ever since Android took over the market by storm, it has emerged as one of the most lucrative platforms for app developers. And thanks to the Internet, developers can now access a host of cheap app development tools and connect with the massive online community of developers. While there never has a better time to become an Android app developer, one must keep in mind that developing mobile applications is quite different from developing web apps. When freshers in Android app development make the shift from desktop to mobile, they often end up making some common mistakes that affect the overall efficiency of their apps.

We’ve compiled a list of seven common rookie mistakes that every Android developer should avoid.

  1. Ignoring The Android Documentation

The Android Documentation is the holy grail for every Android developer because it comprises of numerous helpful tutorials, training videos, informative guides, and other essentials necessary for building Android apps. Thus, if you choose to ignore the Android documentation, you will miss out on expert tips and tricks to develop steady and reliable Android apps. You can either download the documentation with the help of SDK tools or read it online.

  1. Not Building The Compatibility Quotient With Android Tools

Android SDK is replete with helpful tools that help you build great apps. While some tools allow you to create innovative designs and excellent layouts, there are also command line tools that can be used to gain easy access to the device hardware and emulators. Apart from this, Android SDK also consists of tools that help you enhance the performance of your apps and fine tune them. As an Android developer, if you do not familiarize yourself with these tools, you’ll not only have to put in more time and effort, but also you’ll miss out on enormous opportunities to improve your app.

  1. Not Connecting With The Android Community

While building apps, you cannot expect the process to be seamless and free from challenges. There will come times where you may find yourself stuck at a particular step and cannot figure out what to do. Instead of cracking your head incessantly over the issue, reach out to the helpful and proactive Android developer communities such as StackOverflow. You can also try other resources such as Google mailing lists and tutorials available on Android Development Center.

  1. Thinking Android Development Can Be Accomplished In A Jiffy

If you think that developing mobile applications is a matter of little time and effort, you are mistaken. Like web apps, mobile app too requires innovative and functional specs and hence, you need to incorporate advanced bug monitoring system, QA testers, and a release/maintenance strategy for Android apps. The aim is to build apps that are completely optimized for mobile devices and can run seamlessly on Android, and this requires dedicated effort.

  1. Not Integrating The OS Correctly With The Android Platform

One of the most exceptional features of the Android platform is that it offers numerous choices to developers to integrate an app not just with the system but also other apps. As a developer you must ensure that your app is adequately integrated with the OS and hence, you need to stress on small features such as content providers, home screen widgets, intent signals, and so on. When you take care to integrate such tiny details into your app, you create a unique user experience for your users.

  1. Vaguely Defining The App Configuration Details

Often it so happens that app developers do not publish or update the information about app configuration in the Android Manifest. When marketing an app, it is fundamental to enlist all the details about the app features and the device traits they support. Also, developers often demand unnecessary permissions on the Android Manifest, which can lead to faulty app publication on the Android app market, ultimately causing a drop in the user ratings.

  1. Leaving Apps Idle After Launching Them

If you wish to stay relevant in the app market, you just cannot leave your apps idle. Applications must be continuously updated to improve user experience, eliminate bugs and glitches, and to the enhance their overall efficiency. When you do not update and maintain your apps, they fail to keep up with the dynamic trends in the market.

So, now that you know what mistakes you should avoid while developing Android apps, you can steer clear of such pitfalls. And if you wish to step up your app development game, join the Coding Ninjas advanced Android app development course – Envision, today!