A day in the life of a data scientist


Data science is a fantastic mix of art and skill. The skill here we are referring to is digging (not literally) — it’s pure exploration. For a business organization that knows how to analyse the data and work on the problems, an explosive growth is on the way. That’s why there is an ever-increasing demand for data scientists these days. Glassdoor names data scientist as the number one job in the U.S., with a job score of 4.8 out of 5. Also, Harvard business review has declared data science as one of the sexiest job of the 21st century.

To begin your career in data science, you need to be familiar with statistics, data science, Big Data, R programming, Python, and SAS. Hands-on experience with these tools/technologies will set you on the right track.

Talking about a typical day in the life of a data scientist, it won’t be like everyone else, spent doing the same job every day. If you are someone who is flexible and likes daily exploring, then this is the job for you. Every information that travels in the company is valuable and is considered as data with which a data scientist can foresee the future problems and should have the ability to solve it before it even arises. For data scientists, it’s crucial to see the bigger picture from the company’s point of view and build a strategy to leverage the data available for the benefit of the organization.

The whole day of a data scientist revolves around data, which is obviously mentioned in the title. Working with what you already have and bringing out your creative side to solve an unknown issue can be highly rewarding. Data sets are often puzzling and can be mysterious enough to surprise you but finding the actual meaning and getting answers is another one of the valuable returns you can get. It’s about finding out the solution to a problem or just finding something useful which can later benefit the company.

Going with the trends:

Other than spending time with pulling, merging and analysing data and developing or testing new algorithms, a major part of the day of a data scientists includes reading industry related blogs, getting together concepts, building data visualizations, writing different conclusion to share, attending seminars/conferences and basically keep up with the trends. New information comes out every day as data science is one of the most researched fields today. And if it is useful you definitely would want to know.

Here’s what an actual day of a data scientists look like:

Almost 40% of the time is spent on research and development. It also includes checking the social media blogs/vlogs on data science. After knowing all the revised and updated trends, now is the time to focus on developing and testing new algorithms with proofs to solve actual statistical problems. Generally, if a new problem related to data has been solved, it is shared or published in webinars and conferences.

A large chunk of the day will be spent building relationships within the company in order to be aware of all the new projects to come your way. This can lead you to discovering newer problems and being ready with predictive models of the solution. This might just be the most important part of the day as it opens a whole new door to communicate with all the employees and come to the conclusion sooner. As a data scientist, proper communication with a wide range of stakeholders is a very important aspect to simplify the explanations of algorithms to a layman level.

Each project is unique so you can try the project’s initial discoveries to lead you to the next step. Projects can usually be simplified using tools from topology, real analysis and graph theory, which really helps you to speed up rather than coding from scratch. If you don’t have a big team with you, this hack can help you level up and cover more projects than usual.

At last you can always help your team to develop or improve new models by testing the previous ones. Identify the false positives/negatives and emerge new examples to fix the problem. For some cases you should know when to exactly stop as it totally depends on the project for how long you should work on it.

In conclusion…

If the job role we discuss above excites you and makes you want to build a career in the same — bravo! The demand for data scientists is only increasing, like we said earlier, and there couldn’t have been a better time than now to learn and explore. If you wish to start, we recommend you check out our data science course.

Node.js projects for beginners

If you’re just starting out with Web Development, you’d have found yourself confused between the backend language to choose. With so many options, each having their own set of pros and cons, it often becomes a challenging task to pick the one perfect language.

But here’s a catch — no language is perfect, and each have their set of use cases.

Having said that, there’s one particular language that’s gained a lot of fandom in the past few years. That’s especially because the language works on JavaScript — an already extensive tool for building website and web apps. Yes, we’re talking about Node.JS.

Node.JS is an open source runtime environment to write server side applications. You can use it for traditional websites and back-end services. Also, it is efficient and lightweight, and allows you to build highly scalable web applications. It is used for traditional web sites and back-end API services; but was designed with real-time, push-based architecture in mind.

If Node.JS is the language you’ve decided to go ahead with (congratulations!), here are some projects you can work on to get a stronger grip on the language:

1. To-Do list:

Creating a to-do list is a much easier way to understand the basic concepts of a programming language. Create an empty page where the user can record all the task they have to complete during the day. Store the new and completed tasks in a different array. For this application put in very minimal CSS styles with a neat appearance.

To get your application running use express framework. Express is one of the minimalist frameworks which will be very easy to work with a server like node.

2. Portfolio app:

To create a portfolio application using Node.JS, you can first concentrate on the outlook of the application which means how it looks and how are the sample projects working out. It can reveal the sense of style you particularly have. There are multiple elements you can use to give a good experience to your user like presenting the application and the output with a good appearance.

The next thing you have to work on is the architecture of the whole project. It includes the code you are writing to make the application lightweight and easy to use. You have to first define separate routes for each project. Node.js has different set of controllers for each route to manage the views. If you have the same code for the header and footer then you don’t have to repeat them which come as a benefit for the programmers.

3. Chat app:

Building a chat application gives you a very good idea of working with real-time systems. At first, you have to separate the application into two parts i.e., the client part and the server part. With the help of web socket you and the client can send data to each other directly at any time. It works like a virtual handshake. The process starts with the client sending a regular HTTP request to the server. This particular application is very easy to code with the knowledge of web sockets and socket.io.

The following things can help you to enhance your application:

· Keeping a record of all conversations.

· Online/offline labels.

· Take references from the features of whatsapp

· A registration system for one on one chats

4. Web security:

This is one of the most interesting project you can get your hands on if you are about to start working with Node.JS. You can create a spoof login page like Facebook’s to know the passwords of your family and friends. This can be executed if you are able to host it on your LAN. Things get even more interesting when you can host it on the web. Then you can peak into almost anyone’s password.

There is a generous amount of population who usually ignore the links and get caught in these codes.

5. Gaming:

People who are into gaming or at least fancy the idea of how the game works, node.js is one of the languages you can work with. For beginners, the starting point is to code on Node.JS by using web sockets to provide a real time conversation between the clients and the server. If you are an amateur, start with making an applet which collects statistics from multiple clients and put it on a single platform. For larger applications, you can put more effort into CSS stylesheet and have more interface elements. Try to keep all the logic to the server side so the client only has to give input to render the information from the server’s end.

So, that’s pretty much it! Get going with these projects and find yourself in a much better place in terms of coding in Node.JS. If you feel stuck anytime — we’re there! Just hop in to Coding Ninjas where our full stack development course takes care of everything you need to know about Node.JS to start well and go big.

Trends in Machine Learning and Artificial Intelligence

The last couple of years have seen historic improvements in Machine Learning and artificial intelligence. These technologies, and all the others under their umbrella, have evolved from being an unpopular niche to becoming mainstream, and these impacting millions of lives today. Countries and firms are now having budget dedicated to AI and ML for growth and betterment.

This has led to the growth of a lot of trends which were unimaginable before, let’s say, 3–4 years. Let’s see what those trends are:

1. Increase in the number of data science jobs

Machine learning and Artificial intelligence related jobs are hot in demand today. These tops the job chart with a definite gap in supply and demand of resources. There is an immense gap in the required skill and the skill that people possess. This trend will complete change the face of the education sector of India, with more educators turning towards Machine Learning, AI, and related courses to bridge the skill gap.

2. Better approaches to cybersecurity

One think that’s grown along with the rapid growth of data are the progressive hacking techniques. With multiple devices connected to each other via the internet, the systems are becoming more vulnerable than ever. Machine Learning in cybersecurity is a two way street. If deployed by cybersecurity firms, it can increase the levels of security beyond repair. With Machine Learning being an accessible technology, more number of firms are incorporating Machine Learning techniques in their operations and bringing all the more secure systems.

3. Robotic process automation

With intelligent drones and robots dominating the technological revolution space, the human adaptability to Machine Learning is relying a lot on robotic process automation. The benefits of these processes have been felt in everything from finance, health, banking, to even manufacturing — with robots making all the tasks easier. The concern that robots will completely take over human jobs is not entirely baseless but is being proved to be wrong as robots are turning out to be the best counterpart to a human worker — by helping them ease their work.

4. Seamless IT operations

The amount of data being generated through hardware components, software components, server applications, operating systems, and more is nothing less than huge. ML captures this data, cleanes it, and generates intelligent insights that helps businesses grow and become more proactive. Models built on ML and related technologies operate on this data and assist IT operations teams to help solve crucial issues.

5. Transparent decision making

The impact of these machine learning based models and systems in industries such as retail, healthcare, logistics, medicine, and more, has been rapidly felt. The predictive models presented by these algorithms help organizations make transparent decisions — by knowing exact data points. Machine learning through predictive models brings transparency in decision-making in the multitude of domains. Machine learning is an effective tool in transparent decision-making where laws and regulations (for example in HR operations treating job applications equally without consideration of age, gender, religion, caste, creed, color, etc.) come into the picture.

To conclude, 2019 holds a promising opportunity for innovations, especially in the field of AI and ML. These developments will witness faster algorithms and models. And this will undoubtedly open up new avenues for Natural Language Processing, Internet of Things, and self-learning AI. If any of this interests you and you wish to build a solid career in the same, it’s the correct time to jump the gun. Come right in at Coding Ninjas where we offer you a comprehensive course on Machine Learning that’ll help you understand the nitty-grittys of all the latest breakthroughs and trends in ML and AI, and even be a solid foundation for your career!

Reasons why every web developer should learn Node.JS

Web developers are in demand across the world — but it’s the Node.JS developers that get the most attention. So much so that, in the list of most in-demand jobs, the number of jobs related to Node.JS has increased by around 2500% over the last few years.

According to the official Node.JS source, the number of downloads for Node.JS increased by 49% in 2018 alone. There’s no denying that the use of Node.JS in production sites has dramatically increased since its release back in 2010.

With big players like LinkedIn, PayPal, and other tech companies jumping the gun to try out Node.JS and build their platform using it, the language has seen a tremendous increase in its used in web development. It is certainly the more popular language when you compare it with other web-dev technologies.

It can be said without a doubt that for those starting out, Node.JS seems to be the most sensible way. Let’s see some more reasons as to why every web developer (and not only just freshers) should learn Node.JS:

Compared to other web development technologies, Node.js is more popular. For people looking to start their career in web development, starting with Node.js makes more sense than any other. Below are some facts about Node.js that would compel you to learn it.

1. Unstoppable demand

Node.JS, as a language, has seen unprecedented growth in its demand. In fact, it has even overtaken Java = the most popular programming language so far.

The graph above clearly indicates that Java is slowly losing its popularity, while Node.JS is only gaining it. As a developer, you are entitled to say that the comparison between Java and Node.JS is not quite right.

However, if you look at the Stack Overflow Developers Survey that was taken in 2018, you’ll find that JavaScript is the most famous programming language, followed closely by Java. But if you compare the growth of Node.JS and Java, you’ll realize that Node.JS is a clear winner in this aspect.

2. Increase in the number of jobs in Node.js

This point literally follows the first one. If a particular technology is popular, both among developers and companies that require those developers, the number of jobs related to that technology is bound to increase by leaps and bounds.

Node.JS requires less development time, fewer number of servers, and offers an unparalleled scalability — all of which make it an ideal choice for companies looking for a solid backend to their website. Straight from the horse’s mouth, companies like LinkedIn are using it because of the significantly reduced their development time, while Netflix says that it improved their application’s loading time by approximately 70%. Other prominent names in the tech industry that use Node.js includes Medium, The New York Times, PayPal and so on. Gradually, start-ups are catching up on this trend too, incorporating Node.js as part of their technology stack.

3. JavaScript is an extensive language

The best part about Node.JS is the JS. It uses JavaScript which is the most popular and the simplest way to develop applications for the browser. You can ofcourse do it with other programming languages, but none of the offer the ease that JS does.

Generally, if you’re to develop browser applications and server side applications, JS is an additional language you need alongside PHP, Ruby, or Python. Node.JS helps you remove the need for an additional language as it allows you to use JavaScript for both browser-side as well as server-side applications.

Compared to other languages like Python and Ruby, JavaScript proves to have better performance. Add to that the lesser room for human error, as Node.JS helps programmers avoid mental switching between a browser-side language and a server-side language.

4. Get the full stack under your control

Getting control of the full stack requires you to be comfortable with both front-end as well as back-end. Previously, in order to do so, you had to know a server-side language in addition to knowing JS. With Node.JS, however, JS can be used as the base language for all front-end as well as back-end work. Node.JS can be used with JS front-end libraries like Express.js, Angular, and more, you can start working as a Full Stack Developer.

5. Good salary packages

With so many large players recruiting for the Node.JS developer position, the pay scale is also considerably better than that of other technologies. For freshers just starting out in Node.JS development, the salary can be anywhere between 6–10LPA. This entirely depends on your skill set and experience with the language.

Overall, as the demand for Node.JS keeps on growing, there’ll always be a need for a skilled Node.JS developer in some company or the other. It’s the perfect language if you’re looking to accelerate your growth in the industry.

Keeping all this in mind, let’s present to you the Node.JS course offered here at Coding Ninjas. The curriculum is designed keeping everyone in mind and the exercises provided along the way ensure that you have a solid path to walk on. Come on in!

Brain food: Affordable healthy snacks to get your brain running

What is the one thing that programmers around the world can agree on, regardless of the variety of differences they have? The job requires constant and rigorous brain activity — to churn out the best logical solutions. To put the pro in programming, a programmer must have their brain at its best functioning, at all times; and for that to happen, the brain needs special food (like every program needs a special code).

Here’s a list of most efficient, but budget-friendly fuels for your brain, to keep it and your pocket at their healthiest.


Let’s start with the base that will support everything — Water. The cheapest and most essential. Our brain is 73% water, which is enough to convince you how important hydration is for it. Give it what it’s made of! There are studies which indicate a decrement in cognitive performances at dehydration levels of as low as 1%; whereas lower dehydration levels at 2% show adverse effects on visual-motor tracking, short term memory, and a decline in attention. Make sure that your water intake is at least 2.7L — 3.7L (11–15 cups) every day to adequately meet your daily hydration needs.


Foods rich in carbohydrates with lower Glycemic index or GI value (below 70) are the best for your central processing unit — the brain. Prefer whole wheat or whole grain food over white flour food. These are high in carbohydrates and have a low GI value. Here is an abbreviated data of the Glycemic Index for such cheaper and healthier food:

  • Wheat roti — 62 ± 3
  • Chapati — 52 ± 4
  • Barley — 28 ± 2
  • Sweet Corn — 52 ± 5
  • Spaghetti — 48 ± 5

Breakfast Cereal;

  • Porridge — 55 ± 2
  • Muesli — 57 ± 2


  • Apple — 36 ± 2
  • Orange — 43 ± 3
  • Banana — 51 ± 3
  • Pineapple — 59 ± 8
  • Mango — 51 ± 5
  • Dates — 42 ± 4


  • Boiled Carrots — 39 ± 4
  • Boiled Sweet Potato — 63 ± 3
  • Vegetable Soup — 48 ± 5

Dairy Products;

  • Full Fat Milk — 39 ± 3
  • Skimmed milk — 37 ± 4
  • Ice Cream — 51 ± 3
  • Yogurt — 41 ± 2


  • Chickpeas — 28 ± 9
  • Kidney Beans — 24 ± 4
  • Lentils — 32 ± 5
  • Soya Bean — 16 ± 1


Vitamins and minerals are extremely crucial as they work as fertilizer-and-manure for your brain. We fulfill most of our VnM quota through our food, but some essential vitamins such as D3 is only produced and processed when exposed to direct sunlight, so get up, and go out for that stroll. Some other essential vitamins for the brain are B1 (Thiamin) and B9 (Folate) which can be gained by the following;

  • Salmon
  • Barley
  • Papaya
  • Oatmeal
  • Green Peas
  • Soft Tofu (Paneer)
  • Soyabean
  • Lentils
  • Broccoli
  • Mangoes
  • Sweet Corn, and
  • Oranges

Analogously, minerals like Magnesium and Zinc work for our brain in the same way as spinach does for Popeye. You can get all you Magnesium and Zinc from:

  • Whole Wheat
  • Dark Chocolate
  • Spinach
  • Almonds, Cashews, and Peanuts
  • Eggs
  • Chickpeas
  • Lamb

Essential Fats

Contrary to the common belief, the complete elimination of fatty foods from your diet will do more harm than good. While “Trans Fat” is the bad guy, your brain still needs “Essential Fatty Acid” as it is technically a big chunk of fat inside our skull. As a matter of fact, an omega-3 fatty acid, DHA, is a major structural part of our brain tissue. Including food rich in Omega-6 (Linoleic Acid) and Omega-3 (Linolenic Acid) in your diet will prove to be the best plan to guard your brain against corrosion. You can get these from:

  • Chia Seeds
  • Walnut
  • Soyabean
  • Flax Seeds
  • Pistachios


Now that you know the entire code to run your brain without any errors, make sure to keep eating light healthy snacks in proper intervals throughout the day, and exercise regularly.

Incorporate all this in your diet slowly, and develop a habit of healthy eating without going too harsh on yourself (you’re always allowed a few cheat days here and there).

And to get an idea as to how these food items are helping you grow your brain and perform better at your tasks, why not jump straight to Coding Ninjas where we offer you a variety of courses for you to select from? You have the best nutrients coupled with the best mentors with you — there’s no stopping you from rising to the top!

Career prospects after a course on Machine Learning

Machine Learning is emerging as one of the most sought after career choices today. It is likely to create 2.3 Mn. ML-related jobs by 2020 — according to a recent report published by Gartner. The Emerging Jobs Report released by LinkedIn shows that there are approximately 10 times more than ML engineers today than five years age. Artificial Intelligence, Data Science, Machine Learning, and all the other related technologies are some of the fastest growing tech employment areas today.

The most in-demand ML jobs require you to research and develop algorithms that are used in adaptive systems — somewhat like Amazon and their recommendation systems. Companies recruit for positions like ML engineer, ML analyst, Data Science Lead, NLP Data Scientist, and ML Scientist.

Irrespective of the location you’re searching for a job in, chances are you’ll definitely find a well paying Machine Learning job in most of them. Their salaries are reasonably good, too. And add that to the benefit of working in the field you love. From smartphones to chatbots, demand for Machine Learning jobs is at an all-time high, so it is just the right time to get in on the ground floor of a growing industry.

That’s precisely our topic for discussion today. Today, we’ll walk you through some of the best Machine Learning jobs out there for you to brag and grow in!

Machine Learning Engineer:

A Machine Learning engineer creates algorithms that helps decipher information from huge chunks of data. ML engineers are supposed to be competent with Python, Java, Scala, C++, and JavaScript. They must be comfortable in building highly-scalable systems that work on distributed networks. They should keep comfortable in working with teams and building personalized applications and algorithms. As a Machine Learning engineer, you’ll be designing and implementing ML algorithms such as clustering, classification, anomaly detection, or prediction — to address the challenge at hand and produce the best output.

Data Engineer/Data Architect:

Data engineer’s have a completely different job role than an ML engineer. As a Data Engineer, you’ll be responsible for working with an organization’s wholesome ecosystem. You’re recommended to have a working knowledge with Hadoop, MapReduce, Hive, MySQL, MongoDB, Data Streaming. If you have a good knowledge of programming, you already have an added advantage in the job. In addition, you should be proficient in R, Ruby, Python, C++, Perl, and more.

If you work as a Data infrastructure engineer, you’ll be working with developing, constructing, testing, and maintaining highly scalable data management systems. You’ll develop custom applications that can perform analytics on heaps of data. You’ll also be responsible for collecting and storing data, doing real-time processing, and using it all to analyses the data via an API.

Data Scientist:

This is easily one of the most in-demand job professions in the tech. world today. Data scientists are, ideally, experts in languages like Python, R, SAS, MatLab, Hive, Pig, and more.

Being proficient in such Big Data technologies and analytical tools is a prerequisite for the job role. Simply because as a data scientist, you’ll be sifting through large amounts of unstructured data in order to derive insights and help come with better future strategies. Other than that, you’ll also be required to clean, manage, and structure large chunks of data from disparate sources. This, too, requires usage of sophisticated Big Data tools and technologies.

Data Analyst:

Data Analysts are persistent and passionate data miners who have a strong background in statistics, mathematics, and basics of coding. The companies expect a data analyst to be familiar with data storing and retrieval systems, data visualization, Hadoop-based analytics, ETL tool for data warehousing, and other business intelligence concepts. The core responsibilities of of a Data Analyst include designing, deploying, and maintaining algorithms, culling information and recognizing risks, extrapolating data using data modelling techniques, and pruning data.

The Future of Machine Learning

What we talked about are just the paths to get you started. And once you’re on it, there’ll just be growth, for the future of Machine Learning is extremely promising. There is, already, an urgent need of professionals who are not only trained well in Machine Learning, but are also well-versed with the most important tools, techniques, and technologies. If you want to be one of those professionals, prepare yourself by diving deeper into machine learning and get your hands dirty. That is the only way you’ll get deeper into what you learn and be able to prepare yourself for the future that lies.

Whether you’re a programmer, a maths graduate, or even a bachelor of Computer Science, you can land yourself any of these jobs with the relevant skills and knowledge.


These are the most sought-after jobs once you’ve successfully completed a course on Machine Learning and feel that you’re ready to face the job market. Reading through the article would’ve helped you narrow down to the field of your interest. Once you’re there — dive deeper into precisely what does that job role entail, and get going!

Here’s why you should consider ruby on rails for your web development projects!


With the rapid growth of the ‘online’ scenario, websites and web apps are the rage now. And why not?

When you can access anything you need with just a click of a button, be it information or be it that super cool product that’s making headlines — you can get anything and everything online!

This is why smart business owners are readily shifting to the online domain to extend their reach to the vast pool of netizens who’re always on the lookout for awesome websites and applications. Now, coming to the important question — do you know which programming language takes the crown for web dev?

It’s Ruby on Rails of course!

What is Ruby on Rails (RoR)?

Ruby is an Object Oriented programming language and a cross-platform web development application built on top of Ruby. One of the best features of RoR is that it is open-source. Also, RoR leverages the Model-View-Controller (MVC) architecture and comes with a rich array of useful libraries. Such features allow programmers and developers to build highly database driven websites.

Why choose Ruby on Rails for web development?

Here are reasons why you should choose Ruby on Rails for your next web dev project!

1. Super easy to learn!

This is perhaps the most commendable features of RoR that makes it extremely beginner-friendly language. With a simple syntax and code generation style, RoR is very easy to learn and understand. It uses simple English and a domain-specific language approach. So, things are never complicated with RoR.

2. It is open-source.

As we mentioned before, RoR is an open-source framework and hence, it is absolutely free of cost. This makes it an excellent choice for startups and emerging businesses that have limited financial resources.

3. Fantastic libraries!

Another feather in the cap of RoR is its rich and extensive library support. These libraries are so useful that they can speed up the process of web development to a great extent. In RoR, libraries are known as gems which can be easily combined with basic functions of the language. Then there are modules that can be used for arranging classes, constants, and so on.

4. It is highly scalable.

It is a known fact that Ruby is a highly scalable and reliable language that allows you to perform a number of tasks seamlessly. For instance, as Ruby supports the caching function, you can check out the fragment caching within an app’s code. Then again you can use its multi-server automation tool that not only helps to automate variants of a new application but also deploy it to a location. The advantage of the Rails framework on top of Ruby is that it allows programmers/developers to leverage the Chef platform — a cloud infrastructure that helps manage the infrastructure dependencies for the development of folder structure.

5. It is all about clean coding!

RoR supports a simple and neat coding syntax. RoR principles like REST (Representational State Transfer), COC (Convention Over Configuration), CRUD (Create, Read, Update, Delete), and KISAP (Keep It Simple As Possible) allow for faster and cleaner code generation, devoid of any redundancy.

Also, RoR supports agile software development wherein tasks are divided into smaller chunks for the ease of execution.

If these aren’t enough, you should know that RoR is the secret sauce behind some of the most successful websites including Shopify, Twitch, GitHub, Airbnb, Hulu, Zendesk, SoundCloud, Yellow Pages, to name a few. To conclude, RoR is a highly efficient and powerful web development tool that has been embraced by millions of developers across the globe.

You too could master Ruby on Rails by enroling in our inclusive course on Web Development with Ruby on Rails! The course will help you get started from ground-zero. By helping you understand coding logic, and inculcating the thinking of a developer, this course will help you create beautiful web applications with ease.

Also, if you’re just starting out with web development and are looking for the best language for you, let’s also tell you a bit about Node.js. Head over to our blogs to read more about Node and the benefits it offers and make an informed choice!

Emergence of chatbots

Every active Netizen has interacted with a Chatbot at some point in their life. You must have too! Ever noticed those tiny pop-up windows greeting you with a “Hello, how may I help you today?

These are chatbots that have been designed to communicate with you. You must have heard about Siri. What is Siri? She is a super intelligent chatbot (with loads of mindblowing and quirky answers up her sleeve!)

Are you wondering how did these smart and sophisticated chatbots come to be? We’ll break it down for you!

Chatbots — What are they?

Chatbots are essentially a software designed to communicate with humans in their natural language. And believe it or not, chatbots have been around us longer than you can imagine.

The first chatbot — ELIZA — was developed way back in 1966 by Joseph Weizenbaum. He had programmed ELIZA in a way that it could imitate the language and behaviour of a psychotherapist. The next development in the field came in 1972 with PARRY, another chatbot. These early chatbots could interact only through textual commands and responses. The first voice operated chatbot came to the scene in 1988 — Rollo Carpenter’s Jabberwacky project.

As time passed, more advanced chatbots came into being such as A.L.I.C.E and SmarterChild. Today, we have some of the most exceptional chatbots amidst us — Siri, Alexa, Watson, Cortana, and so much more!

The chatbot drive

The latest stats show that chatbots are becoming popular by the minute. According to a Forrester report, nearly 57% of firms across the globe have either already invested in chatbots or are planning to in the near future.

Companies are rapidly incorporating chatbots within their system and rightly so. Chatbots have displayed an immense potential to enhance the entire customer service scenario by leaps and bounds. While Scripted Chatbots work according to predetermined rules, AI-powered Chatbots are way smarter.

The AI-powered chatbots of today use natural language processing to understand human commands (text and voice) and learn from previous experiences. So, a chatbot first soaks in the information you provide to it and then analyzes it with the help of complex AI algorithms, and finally responds to your query with a written or spoken result. As these chatbots have the capability to ‘learn’ from behaviour and experiences, they can respond to a wide range of queries and new commands.

This ability of chatbots has made them extremely appealing to companies who have an online presence and wish to create a more enhanced customer experience.

In a research conducted by Forrester, it was found that messaging happens to be the №1 customer service channel preferred by consumers in the US, India, South Korea, and Singapore. Unlike humans, chatbots can work 24×7 and hence, they can provide round the clock assistance to customers. Chatbots can take over the mundane tasks of human employees and can perform them much better. Also, chatbots can handle several queries at once, so no more waiting in queue for a response to your query.

Using a chatbot can help companies save a ton of money that would otherwise go into paying human employees for performing the same task. According to Juniper Research, chatbots helped businesses save nearly $20 million.

But that’s not all that chatbots can do.

In May 2016, Google unveiled Allo, a smart messaging app. Allo comes with Google Assistant and can help users perform a wide range of tasks, from finding information on the Internet to making dinner reservations! Then again, many companies have started using Facebook’s Messenger bot service that allows them to create a bot in Facebook Messenger that interacts with consumers without them having to ever leave the platform to access another app or another website.

Chatbots — The future

Satya Nadella, the CEO of Microsoft is quite optimistic about the future of chatbots. At the Build 2016 Conference, he stated:

“As an industry, we are on the cusp of a new frontier that pairs the power of natural human language with advanced machine intelligence.”

Judging by the rapid progress that chatbots are making in the present day, it can be safely assumed that in the near future we will get to see even more sophisticated bots (maybe even more so than Siri and Alexa!). For instance, if chatbots are collated with video service apps like YouTube, we may be able to create a new dimension of e-learning wherein the bots would function as learned instructors. With the right mix of technology, there may emerge many such groundbreaking opportunities.

Top Machine Learning experts to follow in 2019.


The technological landscape is rapidly changing. Today, we have concepts, ideas, and realities that were a thing of sci-fi creators’ imagination. We are living in an era where technology is touching (and improving upon) every sphere of our life. Artificial intelligence and machine learning are no more a science fiction concept; these technologies are taking real shape and every day there are advancements in the field. These technologies are revolutionary and can give a new dimension to the way we interact with the machines and the machines interact with us. Several companies have come forward and have invested heavily in AI, RPA, NLP, deep learning, machine learning and AI.

Movies like Tron, Robocop, I Robot, Blade Runner etc. have shown enough how machine interaction takes place and now the time has come when this concept is advancing forward to take some concrete steps in the real world. The world has seen robots like Sophia which was one of the most advanced machines in the world. Machine learning is not only used in the field of technology, but it is being worked upon to be adopted in medicine & healthcare, in the field of biotechnology, education, transportation, travel, media, finance, retail etc.

For all the machine learning enthusiasts, this year is going to be very exciting. In order for you to stay updated with everything that’s happening in the field of Machine Learning, here’s a list of top Machine Learning experts that you should follow:

  1. Andrew Y Ng — The name which probably every machine learning & AI enthusiast knows, Andrew is a computer scientist and entrepreneur. Being the co — founder of Google Brain and chief scientist of Baidu, his contributions have given a new shape to machine learning. He co –founded Coursera. He was a keynote speaker at AI Frontiers Conference in November 2017. His areas of research include deep learning and machine learning. He authored a book Machine Learning Yearning.
  2. Rachel Thomas — Forbes has listed her in “20 Women Advancing AI Research.” She has co — founded fast.ai and teaches data science program at University of San Francisco Data Institute. Hacker News has six times placed her writings on the front page. Her works have been translated into Portuguese, Español and Chinese.
  3. Fei — Fei Li — Li is a computer science professor at Stanford University. She cofounded AI4ALL, an NPO which works in the area of AI. Li specialises in the field of cognitive neuroscience, computational neuroscience, deep learning, machine learning, computer vision and artificial intelligence. She is also the co — director of Stanford Vision & Learning Lab.
  4. Andrej Karpthy — Andrej has expertise in image recognition and deep learning. He did his graduation from University of Toronto and PhD from Stanford University. In 2016, he became a part of OpenAI, an AI group as a research scientist. He became the director of artificial intelligence at Tesla in June 2017.
  5. Yann LeCun — A computer scientist from France, Yann’s work area include computational neuroscience, mobile robotics, machine learning and computer vision. In the world of AI and machine learning, Yann is famous for his works on OCR (Optical Character Recognition). He also founded Convolutional Nets. He together with Leon Bottou and Patrick Haffner, created DjVu.
  6. Pedro Domingos — A professor at University of Washington, Pedro is a famous researcher scientist of machine learning. His areas of expertise are data science, machine learning and artificial intelligence. He is known for his The Master Algorithm and markov logic network. For his remarkable works in machine learning domain, Pedro was elected an AAAI (Association for the Advancement of Artificial Intelligence).
  7. Zachary Lipton — Zachary works as assistant professor at Carnegie Mellon University. He has worked with Microsoft Research Redmond, Amazon Core Machine Learning, and Microsoft Research Bangalore. His areas of interest include core machine learning, applications of machine learning and theoretical foundations.
  8. Ankush Singla — An ex-software engineer at Facebook, Ankush is a Stanford University graduate in Computer Science. Having an experience with Amazon other than Facebook, Ankush has got in- depth deep knowledge about Machine Learning and the concepts that revolve around it.

However, the list doesn’t end here but these are some of the experts who are known for their significant contributions in their respective field of research. The dream of making a machine parse any given word is soon coming true. This year, the world is waiting for such advancements in the field of artificial intelligence and machine learning. We are going to witness more breakthroughs in the field of artificial intelligence, machine learning and NLP.

If knowing about these experts makes you want to be like them, don’t waste a second. If you feel Machine Learning is your calling, jump straight to Coding Ninjas. We offer you online courses on Machine Learning that’ll help you get the push you need.

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