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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

The need of the hour in business world is continuous innovation in the field of cyber security. Security vendors constantly brainstorm ideas and methods that’ll keep them ahead of cybercriminals. The gravity of the problem can be understood from a report by Sophos which mentions that almost 50% of Australian businesses were affected by ransomware attacks in 2017.

To keep functioning amidst such threats, businesses require innovative technologies, and artificial intelligence is one such tool that has become vital for cyber security.

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Artificial Intelligence

AI is a trendy term now, thanks to blockbuster Bollywood movies made on AI!

AI is an all-embracing principle that includes a number of technologies─ machine learning and deep learning being important ones among them. Basically, artificial intelligence enables machines to learn on their own from experience, modify techniques when fed with new data sets and carry out tasks that are human-like. When the principles of AI are applied to cyber security, we call it predictive security. AI helps to identify and check if files contain malware, which is carried out with the help of machine learning as well as deep learning. Although these two branches use similar AI principles, the two fields are fundamentally very different.

Moving on, let’s explore their basic differences.

Machine Learning

Machine learning is an artificial system that learns from examples and generates knowledge from past experiences. ML technology doesn’t simply memorize examples; rather it picks up laws and patterns and applies it later where relevant.

Considering today’s advanced threat landscape, conventional approaches fail to offer strong protection to a system. Malware programs are sometimes designed to make slight changes and breach traditional systems. In such situations, machine learning can be a better security option as it can detect these unknown and modified malwares too.

An important advantage of machine learning is that it keeps evolving and improving as it is used more and fed with more data. Machine learning algorithms scrutinize file elements in order to comprehend the nature of attacks, which includes simple things like file size as well as complex things like part of codes.

Deep Learning

The benefits of employing machine learning techniques in cyber security are numerous. However, it has some drawbacks too, which can be overcome with deep learning. The main limitations of ML are its inability to handle many variables at once, requirement of huge computing powers and using up a lot of space. In deep learning, unstructured data is stored in neural networks and decisions are made using predictive reasoning, which is modeled on the workings of human brain. This structure has potential to manage numerous points of information without hampering speed of the system.

Deep learning can form better idea of the big picture because it doesn’t include programs designed to solve a particular problem, rather it includes mathematical models that learn over time. A model is developed such that it can explain well what it ‘’sees’’. For this, large amount of data is used, such as trends, malicious URLs and other modes of attacks.

Cyber attackers need to be correct in their methods only once in order to breach an enterprise. On top of that, security threats are becoming more innovative each day. Hence, technologies like deep learning and machine learning need to be the founding stones of modern security systems. Understandably, these skills are also very high in demand. Artificial Intelligence certification courses are hugely popular. If this subject interests you, then don’t delay in enrolling for deep learning courses in Delhi or machine learning courses in Gurgaon from leading institute DexLab Analytics.

 
Reference: www.cso.com.au/article/648861/artificial-intelligence-vs-machine-learning-vs-deep-learning-what-difference
 

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

India’s startup ecosystem is gaining accolades. Steering away from the conventional, India’s young generation is pursuing the virgin path of entrepreneurship by ditching lucrative job offers from MNCs and government undertakings – the entire industry is witnessing an explosion of cutting-edge startups addressing real problems, framing solutions and satisfying mass level.

Interestingly, 2018 has been the year of success for Indian startups or entrepreneurs venturing into the promising unknown. Why? In total, 8 Indian startups, namely Oyo, Zomato, Paytm Mall, Udaan, Swiggy, Freshworks, Policybazaar and Byju’s crossed the $1 billion net worth mark this year and joined the raft of most-revered 18 Indian unicorns.

Besides attracting investments from domestic venture capitalists, these startups are bathed in global investments – foreign investors pumped in vast amounts on our homegrown startups to capitalize their activities. Thanks to their generosity, India proudly ranks as the 3rd largest startup ecosystem in the world, next to the United Nations and United Kingdom with its 7, 700 tech startups.

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Nevertheless, our phenomenal startup ecosystem has some grey areas too, which are addressed below:

Startup Initiatives

No doubt, the Indian government is taking conscious efforts to support the startup culture in the country, and for that Prime Minister, Narendra Modi has initiated the Startup India Programme. It is a noble step towards ensuring continuous creation and smooth functioning of fresh startups in India with technology in tow.

Thanks to technology, startups growth seemed to be 50% more dynamic this year!

Fund Generation

As compared to struggling years of 2017 and before, 2018 has been the year of driving investments. India experienced a 108% growth in total funding process, a big jump from $2 billion to $4.2 billion. Though investments at later stages skyrocketed, a decline was witnessed in the early stages during funding companies.

“In terms of overall funding, it is a good story. However, we are seeing a continuous decline in seed stage funding of startup companies. If you fall at the seed stage, innovation is hit. It is the area, which needs protection,” shared NASSCOM president Debjani Ghosh, which remains a matter of concern.

Employment Opportunities

Of course, the new startups push job creation numbers. It enhances the employment opportunities. Of late, NASSCOM reported that the epic growth in startup ecosystem resulted in creation of more than 40000 new direct jobs, while indirect jobs soared manifold. Today, the total strength of Indian startup landscape stands at 1.7 Lakh.

In the wake of powerful female voices and gender-neutral campaigns, our domestic startup ecosystem witnessed how women employees called the shots. The numbers of women employees spiked to 14% from 10% and 11% in the last two years, consecutively.

Global Position

Globally, India ranks as the 3rd biggest startup ecosystem in the world, and Bengaluru is the kernel of tech revolution. A report mentioned India’s significance in recording the highest number of startup set ups after Silicon Valley and London across the globe.

Quite interestingly, 40% of startups are launched in Tier 2 and 3 cities, indicating a steady rise of startup culture outside prime cities like Mumbai, Bengaluru and Delhi NCR.

With technology and startup leading the show, it’s high time you expand your in-demand skills of machine learning and data analytics. How? Opt for a good Machine Learning Course in India. It’s a surefire way to learn the basics and hone already learnt skills. For more information on Machine Learning Using Python, drop by DexLab Analytics!

 
The blog has been sourced from ― www.entrepreneur.com/article/322409
 

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Facebook and Google Have Teamed Up to Expand the Horizons of Artificial Intelligence

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Facebook and Google Have Teamed Up to Expand the Horizons of Artificial Intelligence

Tech unicorns, Google and Facebook have joined hands to enhance AI experience, and take it to the next level.

Last week, the two companies revealed that quite a number of engineers are working to sync Facebook’s open source machine learning PyTorch framework with Google’s TPU, or dubbed Tensor Processing Units – the collaboration is one of its kind, and a first time where technology rivals are working on a joint project in technology.

“Today, we’re pleased to announce that engineers on Google’s TPU team are actively collaborating with core PyTorch developers to connect PyTorch to Cloud TPUs,” said Rajen Sheth, Google Cloud director of product management. “The long-term goal is to enable everyone to enjoy the simplicity and flexibility of PyTorch while benefiting from the performance, scalability, and cost-efficiency of Cloud TPUs.”

Joseph Spisak, Facebook product manager for AI added, “Engineers on Google’s Cloud TPU team are in active collaboration with our PyTorch team to enable support for PyTorch 1.0 models on this custom hardware.”

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2016 was the year when Google first introduced its TPU to the world at the Annual Developer Conference – that year itself the search engine giant pitched the technology to different companies and researchers to support their advanced machine-learning software projects. Since then, Google has been selling access to its TPUs through its cloud computing business instead of going the conventional way of selling chips personally to customers, like Nvidia.

Over the years, AI technology, like Deep Learning have been widening its scopes and capabilities in association with tech bigwigs like Facebook and Google that have been using the robust technology to develop software applications that automatically perform intricate tasks, such as recognizing images in photos.

Since more and more companies are exploring the budding ML domain for years now, they are able to build their own AI software frameworks, mostly the coding tools that are intended to develop customized machine-learning powered software easily and effectively. Also, these companies are heard to offer incredible AI frameworks for free in open source models – the reason behind such an initiative is to popularize them amongst the coders.

For the last couple of years, Google has been on a drive to develop its TPUs to get the best with TensorFlow. Moreover, the initiative of Google to work with Facebook’s PyTorch indicates its willingness to support more than just its own AI framework. “Data scientists and machine learning engineers have a wide variety of open source tools to choose from today when it comes to developing intelligent systems,” shared Blair Hanley Frank, Principal Analyst, Information Services Group. “This announcement is a critical step to help ensure more people have access to the best hardware and software capabilities to create AI models.”

Besides Facebook and Google, Amazon and Microsoft are also expanding their AI investment through its PyTorch software.

DexLab Analytics offers top of the line machine learning training course for data enthusiasts. Their cutting edge course module on machine learning certification is one of the best in the industry – go check out their offer now!

 
The blog has been sourced from — www.dexlabanalytics.com/blog/streaming-huge-amount-of-data-with-the-best-ever-algorithm
 

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Best Machine Learning Questions to Crack the Toughest Job Interview

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Best Machine Learning Questions to Crack the Toughest Job Interview

The robust growth of artificial intelligence has ignited a buzz of activities along the scientific community. Why not? AI has no many dimensions – including Machine Learning. Machine Learning is a dynamic field of IT– where, one gets access to data and learn from that data, resulting into massive breakthroughs in the field of marketing, fraud detection, healthcare, data security, etc.

Day by day, companies are recognizing the potentials of Machine Learning. This is why investment in this notable field is spiking up as much as the demand for skilled professionals. Machine Learning jobs are found topping the list of emerging jobs displayed on LinkedIn – the median salary of a ML professional is $106,225, which pretty much suffices for a well-paying career option.

Importantly, we’ve picked out 5 best interview questions about Machine Learning that’ll optimize your chances of getting hired. Known to all, though ML skill is in high demand, grabbing a job in this booming field of technology is no mean feat. Employers seek particular knowledge and expertise in this field to get you hired. Our 5 best interview questions will help you expand your knowledge base on ML and hone your skills ahead of time.

You can also check out our Machine Learning training course – it comprises of industry-standard course material, real life use cases and encompassing curriculum.

What is Machine Learning?

While you define the exact meaning of the term, make sure you convey your good grip over the nuanced concepts of machine learning, and its real life applications. Put simply, you must show the interviewers how well versed you are in AI and machine learning skills.

What is the difference between deductive and inductive Machine Learning?

Deductive ML begins with a conclusion, and then proceeds towards making deductions about that conclusion. Inductive ML starts from examples and ends with drawing conclusions.

How to choose an algorithm for a particular classification problem?

The answer here is subject to the degree of accuracy and the size of the training set. For a tiny training set, low variance/high bias classifier will work, and vice versa.

Name some methods of reducing dimensionality

Integrate features with feature engineering, eliminating collinear features, or use algorithmic dimensionality reduction – these procedures can definitely reduce dimensionality.

What makes classification and regression differ?

For definite answers, classification is far better a tool. It predicts class or group membership. On the other hand, regression entails prediction of a response.

What does a Kernel SVM mean?

Kernel SVM is the short form of Kernel Support Vector Machine. Kernel methods are basically a specific class of algorithms used for patter analysis and amongst them the most popular one is the Kernel SVM.

Data Science Machine Learning Certification

What do you mean by a recommendation system?

Recommendation system is a common feature for those who have worked on Spotify or shopped at Amazon. It’s an information filtering system that forecasts what a user wants to hear or see, structured on the choice patterns given by the user.

No second thoughts, these interview questions will set you on the right track to crack an interview – but, if you want to gain a deeper understanding on Machine Learning or AI, obtain Machine Learning training Gurgaon from the experts at DexLab Analytics.

 
The blog has been sourced from —

https://www.simplilearn.com/machine-learning-interview-questions-and-answers-article


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DexLab Analytics Partnered With DU for Vishleshan’18

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DexLab Analytics Partnered With DU for Visheshan’18

DexLab Analytics in association with Department of Business Economics, Delhi University proudly presented Vishleshan’18, an analytics conclave to nurture budding talent pool. Each year, Delhi University organizes an annual competition, where in data enthusiasts get an opportunity to showcase their analytical capabilities and complex problem-solving skills. This year, DexLab Analytics shared the platform with the esteemed institutional body under DU – and we can’t feel more obliged!

Our sincere gratitude and good wishes rests with the Department of Business Economics, University of Delhi; they recognized our efforts towards the data analytics community and shared interest in collaborating with us, which was indeed an honorable moment for us.

Now, coming to the event details, Analytics Conclave – Vishleshan’18 was segregated into two rounds. The first round also known as the elimination round comprised of an online quiz session, candidates were required candidates to be well-versed in all verticals of analytics. The second round was a lot more challenging, because here selected teams were allotted a case study each. In this round, DexLab Analytics played a crucial role – the seasoned consultants actively participated in structuring these all-encompassing case studies.

The case studies were all in sync with this year’s theme ‘AI and Machine Learning: Transforming Decision Making’, which means bagging the winner title was no mean feat. Various teams, all from notable institutes and in accordance to eligibility criteria (only post-graduates or MBA students allowed) participated in the contest. Out of them, only 5 teams were finally selected to present their case studies in front of a distinguished panel of judges at the DU campus on 8th September 2018.

Artificial intelligence and machine learning are driving the technology realm. Not only are they the pioneers of effective decision-making processes but also engines of faster and cheaper predictions for all big and small companies. Next to the US, India is deemed to be biggest hub of artificial intelligence, thus it’s time for prestigious Indian educational institutes, like Delhi University to start training the bright young minds for the next big boom of AI and machine learning. And that’s exactly what they were found doing.

However, as it’s said, teamwork divides the task and multiplies the success – the organizers of Vishleshan’18 approached DexLab Analytics, a leading data analytics training institute in Gurgaon, Delhi NCR. Together, they believed they would better analyze the data acumen of the participants and foster a symbiotic association for more knowledge sharing in the future.

Perhaps, not surprisingly, DexLab Analytics has created a place of its own, in the niche analytics industry. Comprehensive in-demand skill training courses are crafted keeping in mind the students’ requirements and industry demands. Moreover, the consultants who bring in considerable domain experience in the related field are all experienced and loaded with expertise. Together with you, this institute can be considered as a center of excellence in the big data analytics domain!

 

For a more detailed report, click the link below:

www.prlog.org/12728482-dexlab-analytics-is-case-study-partner-for-analytics-conclave-vishleshan-18.html  

 

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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

More than 65% of top transportation professionals agree that logistics and supply chain management is in the midst of a revolution – a period of incremental transformation. And, the most potent drivers of change are none other than machine learning and artificial intelligence.

Top notch companies are already found leveraging the tools of artificial intelligence and machine learning for fine-tuning its superior strategies, including warehouse location scouting and enhancing real-time decision-making. Though these advanced technologies nurture large chunks of data, the logistic industry has for long been hoarding piles of data. Today, the difference lies in the gargantuan volume of data, as well as the existence of powerful algorithms to inspect, evaluate and trigger the process of understanding and its respective action.

Below, we will understand how AI streamlines logistics and transportation functionalities, influencing profitability and client satisfaction. Day by day, more companies are fusing Artificial Intelligence with Internet of Things to administer logistics, inventory and suppliers, backed by a certain amount of precision and acumen. Let’s delve deeper!

Predictive Maintenance

AI-powered Sensors monitor operational conditions of machines; thus can detect discrepancies even before the scheduled machine servicing based on manufacturer’s recommendation. Then they alert the technicians prior to any potential equipment failure or service disorientation. Thanks to real-time wear and tear!

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Shipping Efficiency

Powerful algorithms are constantly used to tackle last minute developments, including picking the best alternate port in case the main port is non-operational or something like that, planning beforehand if the main carrier cancels a booking and even gauging times-of arrival.

Machine Learning capabilities are also put to use for estimating the influence of extreme weather conditions on shipping schedules. Location specific weather forecasts are integral to calculate potential delays in shipments.

Warehouse Management

Machine learning has the ability to determine inventory and dictate patterns. It ascertains the items which are selling and are to be restocked on a priority basis, and items which need sound remarketing strategy.

Voice recognition is a key tool that uses AI to ensure efficiency and accuracy through successful Warehouse Management System – a robotic voice coming out of a headset says which item to pick and from where, enabling a fast process of warehousing and dispatching of goods.

Once, the worker founds the item, he/she reads out the number labeled on them, which the system then tallies with its own processed data list through speech recognition and then confirms the picked item for the next step.  The more the system is put to use, the more trained it gets. Over time, the system learns the workers’ tone and speech patterns, resulting into better efficiency and faster work process.

Delivery

 A majority of shipping companies are competing with each other to have the most robust and efficient delivery service, because delivery is the final leg of a logistic journey. And it’s vitally important – predictive analytics is used to constantly maneuver driver routes, and plan and re-plan delivery schedules.

DHL invests on semi-autonomous vehicles that drive independently without human intervention carrying deliverables to people across urban communities. Another company, Starship Technologies, founded by the co-founders of Skype employs six-wheeled robots across London packed with hi-tech cameras and GPS. The robots are stuffed with cutting edge technology, but are controlled by humans so that they can take charge as and when required minimizing any negative outcomes.

Overall, artificial intelligence and machine learning has started augmenting human role for efficient logistics and transportation management. With all the recent developments in the technology sphere, it’s only a matter of time until AI becomes a necessary management part of supply chain.

Data Science Machine Learning Certification

And of course all this excites us to the core! If you are excited too, then please check out our brand new Machine Learning Using Python training courses. We combine theoretical knowledge merged with practical expertise to ensure students get nothing but the best!

The blog has been sourced from:

https://www.forbes.com/sites/insights-penske/2018/09/04/how-artificial-intelligence-and-machine-learning-are-revolutionizing-logistics-supply-chain-and-transportation/#eb663dd58f5d
https://aibusiness.com/streamline-supply-chain-ai
 


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How Machine Learning is Driving Out DDoS, The Latest Hazard in Cyber Security

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How Machine Learning is Driving Out DDoS, The Latest Hazard in Cyber Security

It is common knowledge that the computer world is under constant threat of security breaches. Furthermore, cyber attacks are becoming more dangerous by the day. Over three trillion dollars are wasted every year owing to cyber crimes. And this huge wastage of money is likely to double by 2021. In a time where the number of internet users is increasing exponentially, it seems surreal to expect that threats can be completely eradicated.

Among a plethora of threats, the most infamous one is DDoS, which stands for distributed denial of service attack. In this malicious form of attack, normal traffic for the targeted server, network or service is disrupted by flooding it and its neighboring infrastructure with tremendous internet traffic. This new evil in cyber security has wreaked havoc with business processes.

The tech ecosystem is becoming increasingly dominated by machine learning. ML techniques provide a new approach to eradicate DDoS attacks. In this blog, we discuss a newly researched ML technique that helps restrain DDoS attacks.

SIP and VoIP

A team of researchers from University of Aegean, Greece, headed by Z Tsiatsikas, has published a study about tackling DDoS with machine learning in SIP-based VoIP systems. The popularity of VoIP systems in hardware ecosystems is the primary reason for choosing it for this study. In this age of internet, VoIP is the common choice for voice as well as multimedia communications.

Session Initiation Protocol (SIP) is the preference for initiating VoIP sessions. The basic structure of SIP/VoIP architecture has been described below:

User Agent (UA): This represents the endpoints of SIP, which are active units of the session. For example, in the case of voice communication, the caller and receiver represent endpoints for the session.

SIP Proxy Server: This entity acts both as client and server during the session. The tasks of the server are:

  • Maintaining send and receive requests
  • Transferring information between users

Registrar: Authentication processes and requests to register for UA are managed by this entity.

The VoIP provider keeps a record of the SIP communication. This is an important step as it gives out information to service providers regarding billing and accounting based activities of users. In addition to this essential data, it may also give out data about intrusion or dubious activities happening in a network. Hence, it is very important to monitor this area. If neglected, it may turn into a hotbed for DDoS attacks.

Combining ML Methods in VoIP

The researchers have employed these five standard ML algorithms in experiments:

  • Sequential minimal optimization
  • Neural networks
  • Naïve Bayes
  • Random Forest
  • Decision trees

In the experiment, communications are taken care of through these algorithms. The network is made anonymous using HMAC (keyed-hash method authentication code) and classification features are created. These algorithms are tested using 15 different DDoS attack situations. This is done using a ‘test bed’ of DDoS simulations. The design, as done by researchers, is shown below:

Image source: Analytics India

Following are some of the parameters of the experiment:

  • 3 to 4 types of Virtual Machines (VMs) have been used for SIP proxy, legitimate users, and for generating attack traffic based on the scenario.
  • Particularly for SIP proxy, popular VoIP server Kamailo (kam, 2014) has been employed.
  • sipp v.3.21 and sipsak2 tools have been employed to simulate patterns for legitimate and DoS attack traffic.
  • For simulation of DDoS attack, SIPpDD tool has also been used
  • Weka tool has been used for machine learning analysis.

Performance

Compared to non-ML detection, these algorithms perform well. Speaking from an intrusion detection viewpoint, Random Forest and decision trees work best. With the rise in attack traffic, there’s drop in the rate of intrusion detection, which signifies the presence of DDoS.

To conclude, it can be said that machine learning surpass traditional methods of detecting attacks. This latest development in cyber security is another example of the rapid progress that machine learning is bringing into every field.

Interested in joining machine learning courses in Delhi? Wait not. Contact DexLab Analytics Right Now and get yourself enrolled for the best machine learning training in Delhi.

 

This article has been sourced from: www.analyticsindiamag.com/machine-learning-chasing-out-ddos-cyber-security

 

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LinkedIn Suggests How to Find Machine Learning Experts across Diverse Career Pathways

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LinkedIn Suggests How to Find Machine Learning Experts across Diverse Career Pathways

Machine learning skill is fast picking up pace amongst more and more businesses. Each day, a large number of employees are being sucked into the booming field of big data analytics. But, recruiting them can be a tad bit challenging, on the part of employers. In this regard, LinkedIn recently shared some valuable data that defines the standard career path of a machine learning professional, offering insights as to how enterprises can themselves build and nurture such talent.

In the process of conducting such an intensive analysis, LinkedIn scrutinized various profiles across the globe having at least one machine learning skill listed in their profiles. The analysis of profiles spanned from April 2017 to March 2018.

The result of the analysis is interesting; it highlighted the skills the professionals share with each other and at what point of their career they need to adapt to these skills. It also sheds light on what kind of skills are developed just before machine learning – and they are data mining, R and Python, respectively.

LinkedIn has a valuable suggestion for the recruiters – it says companies can seek job candidates that have these abovementioned skills, only to develop machine learning skill later.

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Some of the other skills worthy of professionals’ interest are Java and C++ – these programming languages are gaining importance day by day.

The data given below even illustrates which industry absorbs the majority of machine learning talent. Unsurprisingly, one third of professionals powered by machine learning skill falls under higher education and research category, more than a quarter of ML professionals are from software and internet industry and the rest are scattered amongst other industry types.

Following the insights, LinkedIn suggests that enterprises should look beyond their respective industries to seek right ML candidates. According to last year’s data, 22% of people possessing ML skill changed their jobs and amongst them, 72% changed industries.

Moreover, the data helps recruiter identify the right candidate by checking out the combination of his skills as a whole and the skills a ML professional should possess. For example, ML professionals belonging from the finance and banking sector are more likely to be specialized in business analytics, Tableau and SAS, while ML professionals hailing from software industry should have a vast knowledge on a broad spectrum of programming language skills.

Future of Machine Learning

Machine learning is another flourishing branch of AI. While the early AI programs were mostly rule-based and human-dependent, the latest ones possess the striking ability to teach and formulate their own operational rules.

2017 was smashing for witnessing growth of scope and capabilities of machine learning, while 2018 harbors potential for widespread business adoption, says a research from Deloitte.

As parting thoughts, AI is nothing but tools adopted to tackle high-end business problems. Designing a proper application of machine learning includes asking the right questions to the right people to get hold of right solutions.

Interested in Machine Learning Using Python? DexLab Analytics is the go-to training institute for all data hungry souls.

 
References:

zdnet.com/article/looking-for-machine-learning-experts-linkedin-data-shows-how-to-find-them

techrepublic.com/article/machine-learning-the-smart-persons-guide
 

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How Python Introduces New Audiences to the Exciting World of Computer Programming

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How Python Introduces New Audiences to the Exciting World of Computer Programming

What was the motivation behind the birth of Python? The language has been searched by American Google users more often than Kim Kardashian in the last one year! And the rate of queries related to Python has trebled since 2010.

Dutch computer scientist, Guido van Rossum, fed up with the shortcomings in commonly used programming languages, developed Python as his Christmas project in 1989. He wanted a language that was simple to read, allowed users to create their own modules for special-purpose coding and then made this package available to others. And lastly he wanted a ‘’short, unique and slightly mysterious’’ name. He named the package after the British comedy group, Monty Python. And Cheese Shop was the chosen name for the package repository.

Nearly three decades after this ground-breaking Christmas invention, the popularity of Python is still growing. According to stats from Stack Overflow, a programming forum, approximately 40% of developers use it and 25% intend to do so. But the programming language isn’t admired by the community of developers alone; it is well-liked the public in general. According to Codecademy, a website that has taught different programming languages to over 45 million novices, Python has the highest demand. Python aficionados, known as Pythonistas, have contributed over 145,000 packages to the Cheese Shop and these cover diverse realms, such as astronomy and game development.

Image source: Economist

Decoding Python’s Fame

Python isn’t perfect. There are other languages that have higher processing efficiency and give users better control over the computer’s processor. However, Python possesses some killer features, which make it a great general purpose language. It has easy-to-learn syntax that simplifies coding. Python is a versatile platform that has a variety of applications.

 

  • The Central Intelligence Agency uses it for hacking
  • Pixar employs it for work related to films
  • Google uses it for crawling web pages
  • Spotify recommends songs with the help of Python

 

Python is also widely used for tasks that are grouped under ‘’non-technical’’. Following are some examples:

 

  • Marketers build statistical models with the help of Python to judge the effectiveness of campaigns.
  • Lecturers use it to find out if the grading system is accurate or not
  • Journalists use codes written in Python for grazing the web for data

 

Professionals who need to trawl through spreadsheets find Python highly valuable for their work. EFinancialCareers, a website dealing with jobs, has reported a fourfold increase between 2015 and 2018 in job listings that mention Python. Citigroup, the reputed American bank, organizes crash courses in Python to train newly hired analysts.

Some of the most appealing packages within the Cheese shop harness the power of AI. Mr. Van Rossum declares that Python is the preferred language for AI researchers. They use it for creating neural networks and identifying patterns from huge data sets. However, the high demand for learning Python comes with certain risks. Novices who know how to use different tools but don’t know their intricacies well are prone to make faulty conclusions without proper supervision.

One solution for this problem is to educate students from an early age. Generally, teaching programming languages is limited to STEM students in American universities. A radical proposal is to offer computer science classes to primary school children. Anticipating a future filled with automated jobs, 90% American parents have expressed desire that their children receive computer programming classes in school.

Presently, 67% of 10-12 year olds have accounts in Code.org. In university level, Python has been ranked the most popular programming language for 2014. While nobody can predict how much longer Python will keep reigning, one thing is for sure, Mr. Rossum’s Christmas invention is truly smart and purposeful.

To the dismay of Pythonistas, on 12th July 2018, he stepped down from the position of supervising the community. The reason being his discomfort with the rising fame!

Well, we hope Python’s glory continues for years to come! To read more blogs on the latest developments in the world of technology, follow DexLab Analytics. If you’re interested in mastering machine learning using Python, then you must check our machine learning courses in Delhi.

 

Reference: economist.com/science-and-technology/2018/07/19/python-has-brought-computer-programming-to-a-vast-new-audience

 

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