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.

WATER

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.

RICH, WHOLESOME FOOD

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

Fruits;

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

Vegetables;

  • 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

Legumes;

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

VITAMINS AND MINERALS

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

ProTip

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.

Conclusion

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.

Placement talk with Saurabh Kumar!

Be a Ninja Coder with Coding Ninjas!

Saurabh_Kumar

His amazing experience and what he loved!

Coding Ninjas really helped me prepare for the interview and brush up the essential concepts during my placements. The lectures are excellent, and the webinars helped a lot. I was in Canada when the course started, and the online lectures were very convenient.

Interview experience

It consisted of 2 rounds where they focused on the Basics of OOPS, DS and the projects I’ve mentioned on my resume.

Advice to Current Students!

Please try to be through with basics and practice coding every day.

How will you introduce Coding Ninjas to your friends?

If you want to become a ninja coder, Coding Ninjas can definitely help you.

Thank you for constant support and guidance!

Best open-source sites to find CSS snippets for web developers

 

If you’re a web developer, a large part of your brain power is spent in coding the basic layout of the front-end. It’s only after that that you’ll be working on the backend functionalities of it. Often, coding front-end items from scratch is a bad idea, especially because there are tonnes of tools available online where you can easily find code snippets pertaining to your need. This is often a time saver for many web developers. But, this can also get extremely tricky if you don’t know where to look.

So, for the same reason, let’s look at some free-resources where you can find readymade code snippets for many front-end elements that will make your work smoother.

1. Web Code Tools

Web Code Tools is one of the best resources for CSS snippets. This site offers custom CSS code generators that help you save time when building gradients, filters, and CSS-based animations.

Another thing about this tool is that it has a massive resource for all frontend development languages. You can find generators and snippets for HTML elements, open graph snippets, and microdata.

2. CodePen

This is clearly the most versatile repository to browse through for code snippets. It comes with a free IDE where you can tweak and play with the code and adjust it as per your liking. It also has a showcase of cool dev projects made by developers worldwide.

The quality on this site is simply amazing and you’ll be able to find almost anything that you want to, on Codepen. You can see the pens that are trending and gaining traction to see what the developers around the globe or up to. Whether it’s for CSS or both CSS and JS, CodePen definitely has you covered.

3. CSS Flow

This tool curates UI kits and design resources. It has a snippets area that contains free hand-crafted code snippets that are mostly geared towards UI elements. These snippets are mostly coded in HTML and CSS/SASS.

You’ll find elements like CTA buttons, toggle switches, signup forms, and even todo lists. You can view all the snippets on your browser before you decide on downloading it.

4. Code My UI

This is a perfectly curated resource to find CSS snippets. All the posts are hand-picked and organized in the order of most recent snippets found all over the internet. You’ll easily fin typography, designs, button styles, custom layouts, and basically everything you need for your website’s frontend to look neat and pleasant.

5. Codepad

The best thing about this tool is that everything on the front page is voted by the users. You can create a new playground if you wish to submit your own code. It gives an online IDE for HTML/CSS/JSS code.

The free CSS snippets vary from simple items like buttons, layouts to more diverse and extensively-designed elements. It also has a collection of beautiful CSS-based loaders for your website.

The sites we’ve discussed in this post are all beautiful and experience them to find your favorite off the list. All of them have sufficient items/elements to help you design a beautiful frontend for your website. Whether it’s forms, loaders, buttons, or whatever you can think of — chances are, you’ll find some of the best-coded elements on one of these sites. Simply tweak it as per your need and you have the website you always wanted!

But that’s just about the frontend. Now comes the fun part — the backend working of the website. What have you chosen for it? Ruby on Rails? Node.JS? Why don’t you check us out at CodingNinjas where we’ll help you not only decide the language for your project but also on your journey towards developing the backend for your website. We cover all the concepts, starting from level 0 — so that there’s nothing you miss!

Python for Machine Learning

 

Machine Learning — a term you’ve probably heard of unless you were living under a rock since the past 2–3 years. But what exactly is it? To put it in simple terms, whenever you shop from amazon for a mobile phone, it suggests you a case or a screen protector for that mobile phone. Or when you buy a t-shirt online it also suggests you jeans and shoes. It predicts your future by analysing your browsing history and time spent on a single product before buying it. There are 3 types of learning algorithms which make it so powerful. Supervised, Unsupervised, and Semi-Supervised Learning. We won’t dive into the specifics of it but basically supervised learning is the data which you give to differentiate between two objects while unsupervised learning is data that hasn’t been labelled by the user. Semi-Supervised learning, on the other hand, is the data which is half-fed for example a large amount with a small amount of labelled data.

Machine Learning has become extremely important in almost every spheres. So much so that it can predict, with certain assurity, when are you likely to have a heart attack based on your electrocardiogram tests. Fascinating, right? Tech CEOs have praised Machine Learning and almost bet that it’s going to be a big deal in the next 10–20 years. Sundar Pichai, the chief executive officer of Alphabet’s Google said he sees a “huge opportunity” in ML. While Jeff Bezos, the richest man on the planet and also CEO of Amazon said, “It’s really early but I think we’re on the edge of a golden era. It’s going to be so exciting to see what happens.”

You can see Machine Learning at work in almost all parts of your life, for example: Google Assistant- the voice recognition assistant uses Machine Learning to differentiate between voices and accents. Amazon Alexa, Amazon dot com use Machine Learning to improve your experience throughout the amazon ecosystem. If you have Face ID on your iPhone X or later, it uses Machine Learning to calculate the number of dots on your face and unlock the phone- all under the span of 10 milliseconds. Cool, huh? It uses what is called Deep Neural Networks to create a mathematical model of your face.It is expected to solve problems that were merely a science fiction back then.

Using Python for Machine Learning

Python is mainly a scripting language that is very easy to learn compared to other programming languages like C++ and JavaScript. It doesn’t require a lot of programming expertise, and if you’re able to think logically, half your work is done — because Python doesn’t rely a lot on syntax.

Python has a larger user base compared to many other programming languages which means more people would be able to understand and participate in your work which makes it easier to work on new technologies from scratch. Developers can find tutorials and tips in the development process. Unlike other languages, Python maintains clean and concise code throughout the project which helps in writing complex algorithms and maintaining them. Python user community has developed many modules to help programmers implement machine learning such as SciKit and Theano. These modules are well-documented to help you step-by-step on your first project. The development is fast and stable which speeds up the process by a lot when fixing bugs.

Advantages of using Python

  • Ease of Use- Python is very easy to use and developers can quickly start adapting to the language.
  • Speed- There is less work required in a code as compared to other languages which saves time.
  • Reliability- The Modules are well documented which makes the experience a bit easier.
  • User base- Thanks to the huge user base Python has to offer, more people can get involved in your project and help iron out the bugs.
  • Huge Library- Already built libraries such as SciKit and Theano makes for a good start for a beginner.
  • Integration through all operating systems- It can run on all modern operating systems through the same code. So for example you could build a project in MacOS, Test it in Linux and upload it through Windows.

Conclusion

In the end, it’s all about personal preference, some people prefer other programming languages for their projects, some people prefer Python. C++/Java can also be used for Machine Learning but it’s the simplicity of Python that makes it the most popular language for Machine Learning.The integration, huge number of libraries and an active community are a huge plus if you’re a beginner trying to get into Machine Learning, Python is a great programming language to kick start and explore the vast capabilities Machine Learning has to offer. It is an extremely accessible language to learn and begin scripting in, important for people coming from non-software backgrounds.

Now that you’re well versed with the benefits that Python offers, it’s time you get yourself started with it. Why not come by and check out the Python courses offered by Coding Ninjas?  We also offer courses on Machine Learning to ensure you’re on the correct path right from the start.

Everything you need to know about website personalization



 

Everyone likes things curated ‘exclusively’ to your website’s users liking is what website personalization is. It denotes the process of creating, curating, and delivering tailor-made experiences for your customers/visitors. So, instead of creating an all-encompassing experience for your entire customer base, you’ll be creating a unique experience for each of your user segment, based on their unique tastes and preferences.

The benefit?

Your customers will feel like you care for them and their individual needs. Not only will their satisfaction quotient increase but you will also gain their loyalty. Also, it will boost your customer engagement to a great extent.

But what are the different types of web personalization?

There are primarily three different kinds of web personalization:

Navigational personalization –

Navigational personalization is a method that leverages a consumer’s browsing behaviour and purchase history. So, by understanding these two patterns, you can easily customize the online experience for your consumers, more precisely, how they navigate around in your website. For instance, you host an online shopping website, and a certain potential customer is eying a particular dress but due to some reason, he/she leaves the platform without buying it. Now that you know that the customer likes that dress, maybe you can minimize its price or provide some discount offer on the dress and prioritize the dress in the recommendation engine in a way that it appears on the forefront. So, the next time the customer visits your site, the chances of him/her buying that dress will increase.

Predictive recommendation –

While shopping on an online portal, you must have come across lines such as “If you like this you might also like this…” or “Customers who brought this…”. Such tags are extremely common on online platforms nowadays. While browsing such lines persuade you to check out similar items on a particular platform so you know you have numerous choices. This is your personalized recommendation engine that predicts your preference pattern and curates specific buying suggestions for you.

Contextual messaging –

Contextual messaging is a method where marketers customize messages for their customers/potential customers based on an array of factors including location, preference pattern, buying behaviour, opening behaviour, to name a few. This allows them to create such messages and emails that capture their consumers’ interests and address their pain points with much higher relevance.

Now that you know the tidbits of web personalization, let’s look at the ways you can create an awesome web personalization strategy!

1. Extract behavioural data and put it to good use!

It is a really frustrating experience for consumers when they find nothing suiting their interests and needs on an online platform. However, you can prevent this from happening by tracking the consumer browsing behaviour, their search history, their preference patterns, and so on. These little cues will not only help you understand your customer segments but they will also help you create the perfect ads and marketing campaigns to best suit their needs. Amazon’s recommendation engine is an excellent example of leveraging behavioural data.

2. Location-based personalization is the way to go.

Another very important way of curating a personalized experience for your niche audience/consumers is through location-based personalization. This is because the needs of consumers vary from location to location. For instance, a consumer residing in a cold country will not be looking for a pair of shorts but rather a warm jacket. Similarly, a resident of sunny California will not be looking for a jacket but rather a pair of cool shorts. You get the drift right? So, your ad campaigns and marketing techniques must be well thought of according to the location of your consumers.

3. Personalized recommendation lists are a hit!

You know what’d attract your potential consumers/audience the most? If you created a website that spoke to them at an individual level and not on a general line. To create personalized recommendation lists, it is first essential to identify your buyer personas. Once you do that, you can develop highly curated and customized online experience for your customers, so much so that it might even transform visiting customers into buying customers!

By implementing these tactics, you can offer a great online experience to your consumers. After all, a satisfied customer is a happy customer. If you go to various lengths to show your customers that you care for them, they will always be back for more. Smart move to grow your business, right?

Don’t wait, start working on it!