Reasons why beginners struggle with Machine Learning and why you shouldn’t!

 

Today, everywhere you look around, you’ll see that Machine Learning and Artificial Intelligence are increasingly diffusing into our lives, so much so that these technologies have become an integral part of it. The manifestations of these technologies are not only fantastic but they are also extremely useful. From smart homes and smart robots to self-driving cars, ML and AI are omnipresent.

This increasing drive towards the ML technology has made it an imperative for developers and aspiring data scientists to master the field. Why? Simply because ML skills take the reigning place among the hottest and trending job skills in the industry right now!

But the thing is, acquiring ML skills ain’t a piece of cake. Even though there are numerous training institutes, online platforms and MOOCs that offer courses in ML, developers find it difficult to grasp machine learning concepts.

Let’s dig deeper into the reasons why mastering ML is a struggle for developers!

1. Math is the real deal.

While it’s true that software development doesn’t require you to use your Math skills (thanks to numerous reusable math libraries and functions), this is the exact opposite with ML. If you wish to master ML, having a strong Mathematical base is a must. You should be well-versed with linear algebra, statistics, and probability.

2. Analyzing data is a toughie.

Data analysis is a part-and-parcel of ML. In fact, a significant portion of Data Science and ML deals in data extraction and analysis.

Thus, when working with ML technology, it is crucial to be able to source and analyze data to extract meaningful information from it. And this isn’t easy. Not everyone can juggle with large datasets, cleanse them, and crunch them into valuable patterns. These steps are what makes up data analysis. Furthermore, having the power of data visualization is mandatory.

3. The eternal dilemma — which language to choose?

Developers are often caught in the eternal dilemma of choosing a programming language for developing ML projects. The debate as to whether to choose R or Python or Julia for ML projects seems to be a never-ending one. However, the truth is, the language choice and preference are best solved by your individual needs and project demands.

Beginners in the field should break the ice with one particular programming language (preferably Python or R) instead of trying to concentrate on everything on the plate. Python/R seem to be a good choice for ML models since they come with rich libraries and many open-source tools that are perfect for developing Machine Learning applications.

4. How to choose the right framework?

Choosing the right ML framework is a challenging task for many developers. This is because there are just so many frameworks and libraries to choose from. Take Python, for instance. It has numerous useful modules such as NumPy, Pandas, Seaborn, and Scikit-Learn, to name a few. Then there’s also open-source tools like Microsoft Cognitive Toolkit, Apache MXNet, TensorFlow, PyTorch, Caffe2, and Keras. For beginners, it is recommended that you begin with a beginner-friendly tool such as Scikit-Learn before jumping onto advanced ones like Keras, PyTorch, and Caffe2.

5. There’s a dearth of development and debugging tools.

As we all know, there are plenty of cool IDEs (Integrated Development Environments) that allow developers to dig deep into the business side of problems instead of cracking their head on how to deal with the environment configuration. Eclipse, IntelliJ IDEA, and Microsoft Visual Studio are such IDEs that offer great development and debugging experience. But the thing is, these developer tools are not optimized for ML and developers must learn to work with a completely different set of tools (for example, Jupyter Notebooks) for ML models. And truth be told, debugging an ML model is way difficult as compared to debugging a conventional model.

6. Which course to choose?

This is yet another dilemma that developers face while switching to ML primarily because the number of courses and MOOCs offered are huge! As a result, one is bound to get confused while choosing courses for learning Data Science and ML. Also, since the field is still developing, no course provides complete knowledge. So, our advice? Do not try to gulp everything at once. Choose a good course and complete it before you move on to another one.

While these are the few reasons why developers today struggle to upskill to ML, you shouldn’t be one among them. How so?

Coding Ninjas has specially curated an advanced ML course for you! Taught by one of the best instructors in the field, this course will not only teach you all the core concepts of ML but also the emerging ones including Supervised Learning, Unsupervised Learning, and Deep Learning. Also, while you explore the latest areas of research in ML, you’ll be given hands-on training on how to solve challenging coding projects. So, by the time the course is over, you’ll be ready to take on the industry with your ML skills!

Don’t waste any more time on procrastinating — come be a Ninja!

Happy Coding!

Everything you need to know about Node.JS

That’s right, today we’re going to enlighten you on Node.js.

We’ll get straight to the most basic question — what exactly is this tool that’s rapidly gaining a massive fan following among the developer community?

According to node.js.org,

Source

Not clear still? We thought so! We’re here to break it down for you.

Node.js, most simply, is a framework that allows you to develop server-side applications using JavaScript as the foundation language. It provides an event-driven and non-blocking I/O.

Because it uses a single thread event loop for handling requests, Node.js can support real-time applications even as they scale. The asynchronous event loop that runs continually makes this possible. Unlike PHP, Node.js is not server load intensive. When you use Node.js, you never have to worry about deadlocks in a process since not a single function of Node.js directly performs Input/Output operations. Hence, the processes always run seamlessly! It is this particular feature that makes Node.js the perfect tool for developing scalable systems.

Node.js: History

Node.js is the brainchild of Ryan Dahl who designed it in the year 2009. Earlier, Apache HTTP servers could not handle multiple concurrent connections. Also, the process in which the code was being generated would either create multiple execution stacks for simultaneous connections or block the process altogether. Dahl wanted a better way out and thus, he developed Node.js. He first demonstrated his creation at European JS Conference that was held on November 8, 2009.

The initial model supported only Linux and Mac OS X. However, in 2011, Microsoft collaborated with Joyent (sponsor of Node.js) to create a native version of Node.js for Windows. In 2012, Dahl decided to pass on the baton to Isaac Schlueter (the creator of npm) who then passed on the management responsibility to Timothy J. Fontaine in 2014. In December 2014, io.js — a fork of Node.js — was created by Fedor Indutny. An internal conflict regarding Joyent’s governance followed this and io.js was made to be an open governance alternative. However, in 2015, the Node.js Foundation came into being and both the Node.js and io.js communities took the decision to function under the same umbrella — the Node.js Foundation.

Features of Node.js

Can run JavaScript externally

One of the many specialities of Node.js is that it can execute a JavaScript code outside of a browser seamlessly! With Node.js, you can use JavaScript for both — writing command line tools and for server-side scripting. So, it runs the scripts server-side to generate a dynamic web page content even before the page is sent to the user’s web browser. In other words, it creates a JavaScript paradigm anywhere, everywhere!

It comes with npm — the largest ecosystem of open-source libraries!

Another awesome feature of Node.js is its Node Package Manager (npm). Essentially, this npm is a storehouse of libraries and other dependencies that have been contributed by the developer community. It is very similar to Ruby Gems. The npm has more than 400k libraries where you can find anything you need to support your Node applications, be it server-side or client-side.

Speedy and efficient

The Node.js framework comprises of smaller modules, with the most prominent ones being Node.JS Core and Node.JS application. You can either use them together or you can use them separately, one without the other — either way, your job will be accomplished fast and without any blocks.

Advantages

Now that you know the most basic features and perks of Node.js, let’s get to know some of its obvious advantages, shall we?

  • The incorporation of Google Chrome’s V8 JavaScript Engine allows for speedy execution of JavaScript code.
  • The event mechanism feature allows you to write and develop highly scalable applications.
  • Node.js is capable of concurrent request handling, that is, it can handle multiple requests simultaneously — thanks to the asynchronous event loop!
  • The npm not only handles the installation of modules but also updates the reusable modules from its vast online collection of dependencies.
  • You can write code in the same language both on the server-side as well as on the client-side. This saves a lot of time when debugging comes into the scene.
  • It comes loaded with tools that allow you to make your application production ready.

Now, why don’t you give it a try and experience the wonders of Node.js for yourself?

Top programming experts to follow in 2019

Hear, hear, all you programmers and coders out there! 2019 is here already, but have you thought about your new year resolution yet?

Well, let us help you with it. This year, your new year resolution should be to hone your coding and programming skills by taking cues from some of the best in the field! We’re talking about some inspiration guys — connecting with the bigger programming community and learning from the pros.

Here are some of the most influential programming experts whom you should follow in 2019!

1. Jeff Atwood (@codinghorror)

Jeff Atwood, the co-founder of StackOverflow and Discourse.org, is famous for his blog Coding Horror. According to his Twitter bio, he’s an “abyss domain expert” who has no idea what he’s talking about. Despite that, the man has around 250k followers on Twitter and they definitely can vouch for his sanity.

2. John Resig (@jeresig)

John Resig is the creator of jQuery and a renowned JavaScript expert. Till date, he has presented more than 125 talks on JavaScript. Currently, he features as a programmer at Khan Academy.

3. Bryan O’Sullivan (@bos31337)

Bryan O’Sullivan works as an Engineering Director at Facebook. Apart from this, he’s also a successful author who has written Real World Haskell. He has also co-authored Mercurial: The Definitive Guide and The Jini Specification. More so, he also lectures at the Stanford University.

4. Rasmus Lerdorf (@rasmus)

Rasmus Lerdorf is the proud creator of PHP programming language. While he developed the first two versions of the language, he made it a point to remain actively involved with other developers in the development of the later versions as well. He was the former Infrastructure Architect at Yahoo! where he worked for over 7 years before joining Etsy in 2012.

5. K. Scott Allen (@OdeToCode)

K. Scott Allen is an experienced software developer having more than 25 years of experience in commercial software development. He has developed web services for startups as well as Fortune 500 companies. He is also an author on Pluralsight and a host on Herding Code.

6. Daniel Ratcliffe (@dantwohundred)

Daniel Ratcliffe (not the actor, that’s Radcliffe!) is a gaming enthusiast and game developer. In his successful ten year career in the gaming industry, he has worked on a host of projects, from solo indie games to heavy AAA console games. Some of his most popular gaming projects are Elite Dangerous, Jurassic World Evolution, Redirection, and qCraft.

7. Tracy Chou (@triketora)

Tracy Chou is all things versatile. She’s a software engineer, an entrepreneur, an investor, and also a diversity advocate — all rolled in one! She has previously worked as a Tech Lead at Pinterest and a Technical Consultant to the U.S Digital Service.

8. Ashe Dryden (@ashedryden)

Like Tracy, Ashe Dryden is also many things rolled into one. She’s a programmer, an author, a diversity advocate, and a community organizer. For over 14 years, she’s been actively involved in web development. At present, she’s busy writing a book on increasing diversity within IT and Tech companies.

9. Amanda Rosseau (@malwareunicorn)

Amanda Rosseau is an Offensive Security Researcher at Facebook. Her areas of interest and expertise include security, malware, and reverse engineering. She has talked on numerous cyber security conferences around the world. So, if you’re interested in contributing to the field of cybersecurity, be sure to follow this smartie!

10. Lara Hogan (@lara_hogan)

Lara Hogan is the co-founder of Wherewithall. Presently, she’s an is an Engineering Leadership Coach. She has previously worked as the VP of Engineering at Kickstarter and also was the former Engineering Director at Etsy. Not just that, she has also authored books on design and public speaking.

There’s no more time for procrastination guys. Get your game face on and start following these uber cool pioneers of the programming world!

Android interview questions for beginners

 

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Any interview experience is often followed by jitters and nervousness. So, if you’re sitting for an Android interview, allow us to get your nervousness in control by walking you through the 10 most frequently asked Android interview questions for beginners:

  1. What do you mean by Android? Also, explain the main components.

Android is an open-sourced OS that enables the development of mobile applications. It is based on Linux and allows users to create and run applications on mobile with the rich high-end components it has. Android has the following main components:

– Linux Kernel
– Android framework
– Android applications
– Libraries

These components enable the developer to create high-end applications which provide all the facilities in a single application with amazing look and feel.

2. What are the important items in an Android project and explain the importance of XML based layouts?

The most important items of any Android project are as follows:
– Androidmanifest.xml
– Build.xml
– bin/
– src/
– res/
– assets/

The two XML files are helpful in providing a consistent layout. They give developers a standard graphical definition format. Generally, all the layout details are placed in these files and other items are placed in the source files.

3. Briefly explain the files and folders that are created during Android project creation.

Src – It contains the java source code for the newly created project. The code for the application that is to be created is also written in this file. It should be made available under the name of the project.
Assets – This folder contains all the relevant information regarding HTML and text files and databases.

Gen – This folder contains the R.java file. This file is generated by the compiler and references the resources that are found in the project. This should not be modified as it is computer-generated.
Android library – This folder contains an android.jar file. This file contains all the libraries required for creating an Android application.
Bin – It contains the .apk file that is created by ADT during the code build process.
Res – This folder contains all the resource files used by the application. It contains subfolders like drawable, menu, layout, values, etc.

 

  1. What is ANR? What are the precautions to be taken to avoid ANR in an application?

    ANR is a dialog that Android shows when an application isn’t responding. It’s short for Application Not Responding. Usually, this state is achieved when the application is working on many tasks on the main thread and has been unresponsive for a long period of time.

Take care of the following things to avoid ANR:

  1. Ensure that there are no infinite loops in case of complex calculations.
  2. Define HTTP timeout for all web services and API calls in order to ensure that the server does not stop responding.
    3) Use IntentService ifthere are many background tasks. They should be taken off the main UI thread.
    4) Keep all database and long-running network operations on a different thread.

    5. Write code for a Toast with the message  “Hello, this is a Toast”.

Toast.makeText(getApplicationContext(), “Hello, this is a Toast”,
Toast.LENGTH_LONG).show();

6. Write code to generate a button dynamically.

protected void onCreate(Bundle newInstanceState) {

super.onCreate(newInstanceState);

Button button = new Button(this);

button.setText(“Button”);

setContentView(button);

});

7. What is AIDL? What are the different data types that AIDL supports?

AIDL is short for Android Interface Definition Language. It is an interface between a client and a service that allows them to communicate using interprocess communication (IPC). It involves breaking the objects into smaller parts that allows Android to understand those objects. This happens because a process cannot access memory of other processes that are running.

Types of data supported by AIDL are:

Map
String
List
charSequence
all data types like int, long, char, Boolean.


8. How would you check for the presence of a Compass sensor on the system using the hasSystemFeature() method?

The sensor framework that forms a part of Android package has Sensor and SensorManager classes. But these classes do not provide the hasSystemFeature() method. So they cannot be used for evaluating a system’s capabilities. The PackageManager class can, in fact, be used to find out information about the application packages available on a given device. One way of checking the presence of a Compass sensor on the system is
PackageManager myCompass = getPackageManger();
If (!myCompass.hasSystemFeature(PackageManager.FEATURE_SENSOR_COMPASS))
{
// This device lacks a compass, disable the compass feature
}

9. Name some exceptions in android?
– Inflate Exception- This exception is thrown by an inflator on error conditions.
– Surface.OutOfResourceException – This Exception is thrown when a surface couldn’t be created or resized.
– SurfaceHolder.BadSurfaceTypeException– This exception is thrown from lockCanvas() when called on a Surface whose type is SURFACE_TYPE_PUSH_BUFFERS.
– WindowManager.BadTokenException – This Exception is thrown when trying to add a view whose WindowManager.LayoutParams token is invalid.

 

  1. What is the difference between Serializable and Parcelable? Which is the best approach in Android?

While developing applications we often need to transfer data from one activity to another. This data needs to be included in a corresponding intent object. Some additional actions are also required to make the data suitable for transfer. For doing that the object should be serializable or parseable. Serializable is a standard Java interface. It is a simple approach where you simply mark a class serializable by implementing the interface and Java automatically serializes it. Reflection is used during the process and a lot of additional objects are created. This leads to a lot of garbage collection and poor performance.
Parcelable interface is a part of Android SDK where you implement the serialization yourself. Reflection is not used during this process and no garbage is created. It is faster because it is optimized for usage on android development, and shows better results.

So, that’s all the basic questions you need to know before sitting for any Android interview. If you stumbled in any of them, we recommend you check out the Android development course offered by Coding Ninjas. It’ll not only clear your basics, but also help you scale beyond.

 

How to deal with an interview rejection and bounce back stronger?

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There’s always an excitement that runs through you every time you get called for an interview. That is followed by nervous energy and then weeks of anticipation. And what if, after all this, all you get is “Thank for you for applying…”?

It feels jerked around – you were given a chance and then it got snatched, just like that. This leads to an array of emotions being set up, ranging from disappointment to despair.

But as much as we’d want to avoid it, it’s a part of our life. And an essential one, if we may. It brings with it an immense potential for your personal and professional growth. So, if you’ve experienced one (and were unable to sanely deal with it) or if you’re going through one – keep the following few things in mind. These will not only stop it from affecting you beyond a limit, but also ensure that you learn from it, and eventually bounce back stronger.


1. It doesn’t define you


Despite knowing that a job rejection shouldn’t be taken personally, it’s often difficult to do so. If the rejection (or the thought of it, for those who’re untouched yet) makes you wonder about your capabilities as a whole, you’ve pretty much let it define you.

And that’s okay. We all have been there.

But, if we put things in perspective employers generally pick the candidate that fits their precise needs. That counts for a lot of things, including your qualification, experience, skills, and many more.

In fact, if there are 10 people sitting for an interview and if they chose just 1, are the other 9 any less?

And to place your entire self-worth in the hands of people is self-sabotaging. When you are turned down for a job, it is not your value that is being rejected; rather, the interviewers may perceive you as not meeting their needs in some way. Perhaps you lack the experience they were seeking, or maybe you were missing some idiosyncratic trait they were looking for. Or just maybe you were off that day and said something goofy. Either way, none of it reflects your self-worth.

2. Knock the job off its pedestal.


No matter how lucrative a job may seem at first, every job has its drawbacks. And if you were turned down for a position you were so eagerly wanting, consider that the job may not be good for you either.

Perhaps there are some skills you need to acquire that the job may not have given you, or there is another position or job that will actually take your career higher. Keep giving other interviews a shot and don’t let any job role/position get to your head. At the end of the day, it’s just a job, and there’s always a learning curve when it comes to interviews.

3. Get good at rejection.


If you’re the one to want to improve your life by putting yourself out there and taking risks, you’re likely to face rejection. Nothing is smooth, and there will be hurdles along the way. Facing rejections is a sign that you’re actually on to something. Of course, this isn’t something that you’ll say to yourself immediately after being rejected – that is the moment of introspection. But, if some time has passed, and the rejection still disturbs you and the only thing it has taught you is sorrow, you need to consider that it’s forcing you to learn to accept it while moving forward.

4. Make the interview process work for you.


When you’re rejected from a job interview, it tends to feel like you put in so many efforts just for them to go in vain. Especially if the whole process was long, gruesome, and drawn out. But, if you really think that you can make the interview work in your favor, you are less inclined to feel like you are left with nothing.

The best way to get the most out of any interview process is to consider that you’ve learned a lot from your experience. Further, you can use that learning to enhance your career pursuits. Just by taking part in the interview process, you are practicing interview skills, and also clearing the concepts required to bag a job. And believe it or not, you’re also building a lot of connections this way. Use all these to make the future interviews work for you.

So, if you’ve ever been rejected, or if you are afraid of rejection – keep the above-discussed points in mind. They will not only help you overcome the rejection, but also ensure that you bounce back stronger, much stronger. Also, if you’re looking to get started with tech interview preparations, Coding Ninjas offers online interview preparation courses where you can get yourself enrolled to cover all bases.  

Four mini-projects for Python beginners

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Learning a new programming language is both exciting and humbling. Especially when it’s a language having as varied use cases as python. So, if you’re on your way to learning python, it’s crucial for you to try your hands on some projects. For a fresher, it makes sense to try out some mini projects before you get your hands really dirty.

So, here’s a list of five mini projects for you to improve your Python skills:

  1. Mad Libs Generator

    This project is inspired by Summer Son’s Mad Libs project based on JavaScript. The program first prompts a user for a series of inputs a la Mad Libs. For example, an adjective, a collective noun, etc. Then, when all the information has been inputted, the program takes that data and places them into a story template.

For this, you’ll need to learn how to use prompts for the user input, and print the full story at the end with all the inputs included. This requires a command on the following concepts:

Strings

Concatenation

Variables

This one’s a pretty fun project that teaches you how to manipulate user-inputted data. As opposed to the prior projects, this is focused more on strings and their concatenations. See what crazy stories you can come up with!

2. Hangman

The actual “hangman” part isn’t really necessary. The main goal here is to create a “guess the word” kind of a game. The user will input letter guesses, you’ll need to limit the number of guesses a user can make.

This means, you’ll randomly grab a word to use for guessing – this can be done from a pre-made list. Then, you’ll need functions to see if the user has entered a singler letter and if that letter is a part of that word. And if it is, then how many times does it appear in the word.

For this, you’ll require the following concepts:


Random
Variables
Boolean
Input and Output
Integer
Char
String
Length

  1. Bootcamp

This will help you dive deeper in your Python programming knowledge. The framework that you’ll need here is the Django framework.

Bootcamp is basically a concept of an enterprise social network. It can be used to help developers or people belonging to one community collaborate and share experiences better. Its motive is to be closed and run inside a company only.

The whole idea will be to have a simple feed, just like Twitter. It will allow users to share links and post their thoughts and also keep track of everyone else in their community. It can also have a QnA section like StackOverflow where developers can post questions related to the business, software, or projects.

Here are a few things that you should definitely include as a part of your Bootcamp project:

Feed App (A Twitter-like microblogging platform)

– Live feed updates

– Comments and likes

– Track comments and activities

Articles App: this can be a collection of resource relevant to the users of the Bootcamp application. It can be a separate section other than the news feed which will contain all the necessary articles.

QnA app (a StackOverflow-like platform)

Messages

– Simple async messages

– Tracking of read/unread

Search app: to help users search through their feed.

  1. Machine Learning Gladiator

Machine Learning Gladiator is one of the fastest ways to build practical intuition around ML.

The goal of this project will be to take the out-of-the-box models and apply them on different data sets. This project is completely amazing, especially for beginners, for three main reasons:

First, you’ll get to build intuition for model-to-problem fit. This will help you understand which models are robust to missing data? Which models handle categorical features nicely? Ofcourse you can read through the theory and find the answer, but it’s always better to learn by seeing things in action.

Second, the project will help you acquire the invaluable skill of prototyping models in real-time. When it comes to real-world problems, it’s often difficult to know which model will work best for you without trying it out.

Finally, this project helps you master the workflow of model building. You’ll get to practice:
– Importing data
– Cleaning data
– Splitting it into train/test or cross-validation sets
– Pre-processing
– Transformations
– Feature engineering

Because you’ll use out-of-the-box models, you’ll have the chance to focus on honing these critical steps.

For this project, you can use the following data sources:

UCI Machine Learning Repository – It contains a collection of more than 350 searchable datasets that cover almost every subject matter. You’ll surely find datasets that fit your need.
Kaggle Datasets – This contains more than 100 datasets uploaded byt he kaggle community. There are some really interesting datasets here, including PokemonGo spawn locations.

All of these projects are pretty basic, and can get you up and running in the world of Python programming. Further, if you find yourself stuck anywhere while trying to develop these games, do drop by at Coding Ninjas where there are courses around Python. These courses cover the concepts of Python with data structures, and are good enough to take you from ground zero to the top.

Happy learning!

Career Opportunities after mastering Ruby on Rails

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Ruby on Rails (RoR) is a technology which is fresh and gaining fair attention in the market. Ruby on Rails is predominantly a server-side web application framework written in Ruby. Rails offer a default arrangement for a database and web pages. Ruby on Rails is more celebrated these days than it was 5-6 years ago. So, techno-enthusiasts are quite interested and keen to absorb and master this web technology.  

Ruby on Rails was initially released in 2005 which inspired and influenced web application development through its groundbreaking and modern features related to the database.  Ruby on Rails has kits which reliefs the difficult web development tasks. One such feature is, ‘scaffolding’ that can spontaneously build various prototypes required to run an elementary website. Due to its out-of-the-box features, it is making its own unique path and gaining popularity among the developers.

Ruby on Rails has several tools which include less coding and more creativity. Developers will enjoy while experimenting with the tools which will give anchors to new ideas and enhance them. In this framework, there is an independence of using new layouts and therefore it increases the horizon of learning new things along with giving utmost satisfaction in the career.

There is a huge demand and competition in the market for developers who have fluency in trending programming languages. And surprisingly, the developers who have mastered the framework of Ruby on Rails get a bigger package than any other developer. So, it is a good career option for the people who are planning to jump into the application and development field. Ruby on Rails is gradually becoming one of the most popular web app frameworks. At this rate, the demand of RoR developers is going to increase indelibly. Many firms like Scribd, Twitter, EBay etc. have already made the most of this technology and are quite satisfied with the results. Similarly, smaller organizations are preparing to switch and join the RoR fan crowd.

It literally pays to know Ruby on Rails. This can be justified very well because according to Indeed, the average salary for RoR is higher than 86% of the average salaries of other companies. This is pretty impressive for a decade old technology. We can only imagine the number of career options it has opened up for the youth. Developers of RoR get paid more even for entrance level posts. This is also because of the fact that Ruby on Rails’ framework works on an open source dais, the companies don’t have to invest a lot in this technology.  

In a nutshell, there are several Ruby on Rails jobs for the youth who have mastered the art of this framework like, junior programmer, co-developer and high-level jobs such as, Project Lead, Chief Technology Officer and Senior Developer. The requirement for the Ruby on Rails developers has increased by several times in the former years. This skillset can prove to put an extra star on the developer’s resume. This is a type of technology in which the coders and developers can easily jump in without having the fear of growth and finances.

5 AI and ML startups hiring in 2018

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Elon Musk had once stated that “AI will be the best or worst thing ever for humanity.

And today, we’re witnessing the many, many, marvels of Artificial Intelligence that are ruling our lives. From the IT sector to banking and fintech, from healthcare to education, from e-commerce to the business sector – AI has penetrated every major industry across the globe. The pace at which AI is escalating is simply stunning. Since 2000, the number of AI startups has increased by 14 times, and since 2013, the number of jobs demanding AI skills has grown by 4.5 times! Furthermore, Statista maintains that between 2017-22, the global market revenue of AI will grow by tenfold.

Moving on to the Indian AI startup scene, it can be safely said that the market is booming at an impressive pace. According to Inc42 Data Labs 2017 report, the AI startup sector saw a 100% increase in 2017 as compared to 2016 (with respect to deals attained and closed). Bengaluru takes the limelight here – nearly $88 million was obtained from over 58 deals in the AI sector.

Here are AI startups in India that have made their mark in 2018!

  1. Niki.ai

Niki.ai is a Bengaluru-based startup founded by four ambitious alumni of IIT Kharagpur. It is essentially an AI-powered personal shopping assistant that helps users shop for products and services online by leveraging Natural Language Processing (NLP) technology. The services offered by Niki.ai span across twenty differentiated categories such as travel, entertainment, shopping, and much more.

It has partnered with many reputed brands including HDFC, BookMyShow, OYO, Redbus, Ola, and Uber, to name a few. Furthermore, it has obtained funding from business moguls like Ratan Tata and Ronnie Screwvala.

  1. Absentia VR

Another Bengaluru-based AI startup, Absentia VR is the creator of Norah AI – a revolutionary, state-of-the-art, AI-empowered workflow that allows for both game creation and incorporation of game elements for enhanced story weaving and texturing. At its core, Norah has AI, Deep Learning, and Neural Network.

It was founded in 2015 by three college dropouts from BITS Pilani with an aim to create the first unique AI gaming experience in India. Like Niki.ai, Absentia too has obtained funding from renowned investors including V. Balakrishnan, Girish Paranjpe, Astarc Ventures, Exfinity Venture Partners, and 50K Ventures.

  1. Innefu Labs

Innefu Labs is a Delhi-based AI startup that provides cybersecurity solutions including multi-factor authentication, biometric authentication, and Big Data analytics to both predict as well as prevent cyber threats. Rising cyber threats are a serious issue for companies today and to address this, Innefu has developed its patented security solution – AuthShield that leverages image and speech-based authentication system. AuthShield works something like this – if a user wishes to access an application/system, he/she first needs to click a selfie from the phone and speak a few words. The image and speech are then matched and verified with the user’s features by a 3D modeling pattern and if it matches, access is granted.

Innefu caters to the security needs of clients spanning across diverse industries such as the Indian Army, intelligence agencies, police departments, e-commerce, IT/ITES, telecom, BFSI, and BPOs, among others.

  1. NetraDyne

NetraDyne is a one-of-a-kind commercial vehicle technology provider headquartered in Bengaluru and San Diego. Its IoT-based product, Driveri combines AI and Deep Learning to identify and determine the causality of unfortunate accidents on the road by providing commercial fleets with real-time and meaningful data. With the data at their disposal, commercial fleets can detect risky driving and the possibility of unforeseen incidents and prevent it from happening by applying their resources promptly. The aim is to enhance commercial fleet safety.

In June 2016, NetraDyne obtained $16 million in funding from Reliance Industries Ltd. (RIL).

  1. Staqu

Staqu is a Gurgaon-based AI startup specializing in advanced image and video analysis, speech recognition, facial recognition, text processing, and language and text-independent speaker identification engine.

The core focus areas of Staqu include Deep Learning, NLP, Computer Vision, and image processing. Combining these innovative technologies together, Staqu aims to enhance a user’s mobile experience. The AI startup has developed a pioneering contextual content recommendation engine designed to provide in-platform and cross-platform recommendation facilities to smartphone companies while giving the users complete control of the platform too. Apart from a smart recommendation engine, Staqu also provides a robust security framework with fully-customizable facial and speech recognition features.

These are only five among the numerous AI startups that are rapidly transforming the AI scenario in India. As such innovative startups continue to rise and shine, it can be safely said that India’s future looks bright with AI!

 

An introduction to chatbots

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If you are an active user of social media and messenger apps, you must be familiar with chatbots.

That’s precisely our topic of discussion today – Chatbots!

In the past two years, the entire experience customer service has scaled up a notch, thanks to chatbots. These interactive bots are now used by almost every major company like Facebook, Apple, Google, and Microsoft, to help customers interact with their brand on a personal level and know more about their products and services. In other words, chatbots have paved the way for a more personalized online experience.

According to Gartner, by 2020 nearly 85% of the customer interactions will be handled without any human assistance. How? Chatbots, of course!

Now, let’s get to the fundamental question:

What is a Chatbot?

To put it in the simplest of terms, a chatbot is a program or software that can communicate with you (and other humans too!).

But wait, don’t be mistaken. Although a chatbot is designed to communicate with humans, it is unfair and even unjustified to think that a chatbot can communicate with you exactly as a human would! For no matter how advanced a bot is, it is what it is – a bot. So, it’s better you don’t harbor any unrealistic expectations from a chatbot!

Chatbots are basically of two types:

Scripted (rule-based)

Scripted chatbots are bots whose behavior and functionality is determined according to a specific set of rules. Hence, any conversation with scripted chatbots will follow a predetermined course for they will know only how to answer according to the way they’ve been programmed. So, when you communicate with such a chatbot, its answers and options (voice, text, touch) will depend on its innate design and preset responses.

Intelligent (AI-powered)

Intelligent chatbots, as the name suggests, are intelligent. Being AI-powered, these chatbots are designed to learn and adapt from behavioral experiences. So, the more you interact with them, the smarter the bots become. For instance, if you wish to have the recipe of chocolate cake, the intelligent chatbot will surf the Internet and find all the best possible resources for a chocolate cake recipe for you! Unlike scripted chatbots, intelligent chatbots can accept any free form of input (text, speech, or image) and respond accordingly.

However, one thing to keep in mind is that both of these chatbot variants have their own limitations as the technology is still largely in its infancy.

History: The beginning of chatbots

The first breakthrough in this arena dates back to 1950 when Alan Turing and Joseph Weizenbaum imagined computers talking like humans and with that in mind, they developed the Turing Test – a test designed to distinguish a human from a machine.

However, it was only in 1966 that Weizenbaum programmed the first chatbot named ELIZA that could imitate the language and behavior of a psychotherapist. Another chatbot named PARRY came into the scene in 1972 and interaction with both ELIZA and PARRY had to be done in the form of texts. Voice operated chatbot came much later in 1988 with Rollo Carpenter’s Jabberwacky project.

Fast forward to the present day and we have some of the most developed and intelligent chatbots such as U-Report, Heston Bot, Nerdy Bot, ENO, and to name a few.

What does the future look like for chatbots?

The future of chatbots certainly seems bright now for people are now showing more inclination towards messenger apps than social networking apps.

 

So, businesses are naturally shifting to the messenger and chatbot domain than investing their time and resources on social media platforms. Chatbots now present a huge and emerging business opportunity for any and every individual who’s willing to leverage its potential.

Now the question is, are you ready to cash this opportunity?

Difference between a programming language and a scripting language

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Often people use the terms scripting language and programming language synonymously, but these two terms have more difference than you know of. Although all scripting languages are programming languages, all programming languages are not scripting languages.

Earlier, programming languages were written to build products such as PowerPoint, Internet Explorer, Microsoft Excel, and Microsoft Word, to name a few. However, as time passed, there arose a need to upgrade the programming languages to incorporate additional functionalities and provide an enhanced programming interface. Thus, scripting languages came into being. The primary difference between a scripting language and a programming language is in their execution – programming languages use a compiler to convert the high-level programming languages into machine language, on the other hand, scripting languages use an interpreter. While a compiler compiles a code in a complete chunk, an interpreter compiles a code line by line.

Let us now look at some of the major differences between a programming language and a scripting language.

Definition

A programming language is essentially a formal language that combines a set of instructions that can be fed into the computer to generate a specific output. A scripting language is a programming language that supports scripts which are programs written exclusively for a special runtime environment to automate the execution of a specific action/function.

Interpretation

Programming languages are compiled into a more compact design that does not require to be interpreted by another language or application. Scripting languages are written in one language and interpreted within another program, for instance, JavaScript has to be incorporated within HTML which will then be interpreted by the Internet browser. Thus, programming languages run independently of a parent program, but scripting languages run inside another program.

Design

Programming languages are designed to facilitate a full-fledged code and software development whereas scripting languages are specifically designed to make coding faster and much simpler.

Development

Coding with programming languages is relatively difficult as many lines of code are required for a single function. Creating a code function with a scripting language is easier as it requires only a few short and specific lines to be written. Thus, writing a  full-fledged code with programming languages usually take a longer time to develop as more lines need to be written while coding with a scripting language requires less time as smaller chunks need to be written.

Categories

Programming languages are divided into five subcategories: First generation, Second generation, Third generation, Fourth generation, and Fifth generation. Scripting languages have only two subcategories: Server-side scripting languages and client-side scripting languages.

Conversion and hosting

Since programming languages use a compiler, it is a one-shot conversion. Scripting languages, on the other hand, demand line by line conversion. Programming languages are self-executable; they do not require a host. Scripting languages require a host.

Speed

Compiled programs run generally run faster than interpreted programs since compilers read and analyze the code at once and report errors (if any) collectively. An interpreter, however, reads and analyzes a code line by line and every time it detects an error, it stops to address them one by one.

Languages

C, C++, C#, Java, Basic, COBOL, and Pascal, are some examples of programming languages. JavaScript, Perl, PHP, Python, Ruby, Rexx, Ruby, GameMonkey, etc., are some of the most widely used scripting languages.

Conclusion

Overall, as we mentioned before, the execution process and environment are the primary differentiating factors between programming languages and scripting languages. Programming languages are used to develop applications and scripting languages are used to write code that can enhance and control application behavior.

So, now you know how programming languages differ from scripting languages, you’re ready to pick one for you. But again, you should keep in mind that the language you choose will depend entirely on your use-case and what you wish to accomplish with it. If you find yourself confused, don’t hesitate in visiting Coding Ninjas, where we have courses on programming languages including C++, Java, and scripting languages like Python, Ruby on Rails. We’ll ensure you reach all the way to the top, no matter where you start from!