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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.

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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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A Comprehensive Guide on the Functioning of Chatbots

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A Comprehensive Guide on the Functioning of Chatbots

Chatbot is a technology that is rapidly growing and is likely to power 85% of customer service by 2020. And this is already mid 2018. Though this technology is booming, many are new to the concept of chatbots. To help such newbies, on this blog we will discuss what a chatbot really is and also talk about the different parameters related to it.

So, what is a chatbot?

A chatbot is a computer program that interacts with human users through simulated conversations using the Internet. The chatbot cannot set commands by itself. It simply provides solutions to human queries through the most natural medium of communication, which is chatting and messaging in the language of customers.

The next question that comes to our mind is-

What are the tasks that a chatbot can perform?

In this regard, it must be kept in mind that chatbots are basically programs that automate tasks. The tasks span over a variety of fields, including customer support, appointment scheduling, performing surveys and lead generation. Here are some areas of the business areas where chatbots have been very beneficial:

  1. A chatbot answers FAQs and gives the information customers want about different products and services. In short, businesses keep chatbots to handle all the customer queries. In fact, bots are able to respond to multiple queries at a time!
  2. It helps customers schedule appointments, plan trips and informs them if a product is available or not.

It has been found that companies that use the services of chatbots can save up to 60% of their time!

Why have Chatbots become the talk of town?

Most important reason for their growing popularity is that they allow the company to be present on a platform that is extensively used by customers– online. With the advent of chatbots, brands can be in the same space as their customers, without being physically present. Customers are able to interact with businesses 24/7. Thus, bots act like sales representatives online that are ready to assist customers. This directly leads to higher sales for many businesses. Moreover, chatbots respond depending on the industry it’s employed in and the customer it’s interacting with. Hence, it helps deliver personalized responses to every single user.

Working of a chatbot:

Chatbots are basically a form of AI that is developed by means of complicated programming. There are two main types of chatbots. Some chatbots function through a set of structured questions and answers and some function mainly through machine learning algorithms. The later is more complicated. However, both may look the same to users.

Scripted and structured bots: The chatbots working with structured question and answers have a limited knowledge base. Their skills are limited to correctly answering only specific questions which the bots are programmed to answer. There might be questions that aren’t included in the programming, to which the bot is likely to respond with ‘’I’m sorry, I didn’t understand the question.’’ These bots are as smart as the programming behind them permits. These types of bots are generally used for marketing in Messenger platforms. They perform tasks like sending daily mails and content pieces, generating leads, performing surveys, etc.

Source: DZone

NLP based chatbots: These bots understand language very well and deviations from the standard set of questions won’t baffle them easily. NPL (natural language processing) is a part of machine learning and the incorporation of NPL is what enables these bots to understand the nuances of language so well. Obviously, it takes a lot more work to develop these intelligent chatbots. There are three main concepts in NPL- intent, entity and utterance. Intent and entities are responsible for structuring the chatbot, whereas utterance is responsible for improving the bots with use. The best part about machine learning chatbots is that the more they are interacted with, the cleverer they become.

With the availability of free DIY chatbot platforms, chatbots can now be created without prior knowledge on coding. But, if you wish to be a pro in this field then acquire the necessary skills through the machine learning training in Gurgaon. For all the trending news on big data and related tech, follow DexLab Analytics. We are an institute that provides high-quality machine learning courses in India.

 

Reference: dzone.com/articles/here-is-a-complete-guide-of-chatbots

onlim.com/en/how-do-chatbots-work

 

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Python Is Gaining Popularity against SAS, R – Says Burtch Works

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Python Is Gaining Popularity against SAS, R – Says Burtch Works

Python is on the rise – though R and SAS are languages of choice amongst the data scientists but R is soon ascending the steps of analytics ladder. Already a lot of practitioners and data scientists have armed themselves up with this incredible R Programming tool for future career aspirations. To add volume to the statement, we’ve a new survey from a high-end recruitment agency, Burtch Works – let’s see what their comprehensive report says about our preferred language.

The survey began with R, an open source tool and SAS, another commercial tool. Later in 2016, Burtch Works added another open source tool, Python.

This year, however we witnessed something that never happened before. There’s no clear winner, this time – Python stood at 33%, R at 33% and SAS at 34%. “This is the first year that we’ve seen SAS, R, and Python all at the same level of preference,” said Linda Burtch, a quantitative recruiting specialist and Managing Director at Burtch Works.

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According to the results, R declined slightly as compared to last year figure, whereas SAS remained fairly flat. On a positive note, Python continued reflecting an increasing trend over the last two years, since its inclusion.

“The most noticeable trend from the 2018 data was Python’s ascension, and how Python’s growing popularity has been eroding support for R,” Burtch shared with InformationWeek. “Data scientists have typically strongly preferred Python, but predictive analytics professionals working primarily with structured data are shifting that way as well.”

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But what makes Python so fetching? It is considered to be a very strong language for machine learning, perfect for data visualizations and other statistical applications, better than SAS and R. Budding professionals enjoy working with Python(48%) as compared to R(38%) and SAS(14%). Survey reveals that open source tools, such as R and Python are in-favor of professionals who are young and new in technology. 

Going by the survey results, the use of R has fallen drastically from 50% in 2016 to below 40% this year. At the same time, the growth of python has been phenomenal – in 2016, it was standing at 20% and this year, it is hovering around 50%.

“Python gained support in almost every category we examined this year and has especially taken hold at the early career level, with professionals who have five or less years of work experience,” Burtch concluded to InformationWeek.

As parting thoughts, Python is considered to be a very versatile programming language. Its popularity soared in recent years – its usage and employability knows no bounds. For beginners and newcomers, it’s like a treasure trove waiting to be discovered. So, if you are one of them, it’s high time to consider a Machine Learning Using Python certification program – easy to learn and highly accessible, Python programming is ideal to get started. Most importantly, its simplified syntax with an undue focus on natural language is an added bonus.

 

The blog has been sourced from – 

informationweek.com/big-data/ai-machine-learning/python-gains-on-sas-r/d/d-id/1332331

kdnuggets.com/2017/07/6-reasons-python-suddenly-super-popular.html

 

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Adopt Machine Learning and Personalize Marketing Game Big Time

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Adopt Machine Learning and Personalize Marketing Game Big Time

In the last couple of years, Netflix and Spotify have altered our digital expectations. The technology that these fast-growing streaming media companies use to generate fulfilling customized experiences is a particular kind of Artificial Intelligence, known as Machine Learning.

Highly technical though it sounds, Machine Learning is the most valuable, new-age tool that all the marketers need to employ right now. To better explain the nuanced concept, we’ll start with an approach that preceded it.

Human-based Marketing: Limited Scope

Previously, rules and segmentation used to dominate marketing domains; most of the customized experiences in the past were delivered through a set of norms, created manually by a marketer based on some predetermined criteria. Though the approach worked, but its scope was very limited.

The hitch is that the humans wrote the rules, based on what they believed true and right. But, remember, each human being is unique, and so is their perception. Also, their intent varies from time to time. In short, there exists too much data for a normal human being to assess or sort without taking the help of machines, or in this case Machine Learning.

The Rise of Machine Learning

Instead of relying on human intuitions, machine learning algorithms offer an innovative way for marketers to curate incredible experiences for individuals. No longer does the computer follow any rules and commands, rather we’ve programmed it to learn everything about a particular person, so that it can conjure up the experience that appeals to him the most.

For improved machine-learning personalization, marketers should build and feed in own ‘recipes’ to the computers that tell the kind of information to consider, when formulating someone’s digital campaign.

 Sometimes, the algorithms can be pretty simple, such as showing trending topics or they can be very complex, like decision trees or collaborative filtering. It all depends on the marketers to devise a strategy that would ensure the best customized experience for the visitors, of course with Machine Learning using Python.

Decision-making Induced by Machine Learning

When you speak with a person, you know what to say next and when to stop, based on the idea of previous encounters with him/her. Now, if it’s for the first time you’re speaking with him, you behave in a way you are expected to, based on social interactions with others.

Machine learning functions in the same way. Based on recognition and remembering past situations, this type of learning creates a fluid pattern that controls next behaviors.

It uses real data to derive at decisions, just similar to a normal human being who would come to a conclusion after a conversation.

As parting thoughts, humans shouldn’t hand over everything to the machines; machine learning can be all so rosy and perfect, but it’s us who needs to define, examine and refine the algorithms to make them work and fulfill the overall objectives of one-to-one customization and superior brand experience for the clients.

Of course, machine learning has over-the-top advantages against traditional human-based approaches, but it’s us who have developed them. And that matters!

For business analyst training courses in Noida, drop by DexLab Analytics. They are specialists in a number of in-demand skills, including big data hadoop, SAS and R programming, amongst others.

 

The blog has been sourced from – https://www.entrepreneur.com/article/311931

 

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Top 5 AI-based Applications for Crime Prevention and Detection

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Top 5 AI-based Applications for Crime Prevention and Detection

Companies and cities across the globe are attempting to employ AI in a plethora of ways to address crime. Day by day, city’s infrastructure is becoming smarter and tech-efficient. Crime detection is no more a catch-22. With easy availability of real time information, it’s now easier to detect crimes.

Here, we are going to dig into a few present AI applications in crime detection and prevention:

Gunfire Detection – ShotSpotter

ShotSpotter utilizes smart city infrastructure to pinpoint the area from where the gunshot came through. The company representatives claim that their system has the ability to alert authorities in real time with the data about what kind of gunfire it was and the exact location as accurate as 10 feet. Thanks to multiple sensors and their machine learning algorithm. They work by picking up the sound of the gunshot.

At present, they are being used in over 90 cities across the world, including Chicago, New York and San Diego.

AI Security Cameras – Hikvision

China’s top notch security camera producer, Hikvision made an announcement last year: they are going to use chips from Movidius (an Intel company) to develop cameras that would run intricate, deep neural networks right away.

They claim this new camera would better scan the license plates on cars, perform facial recognition for potential criminals and automatically identify suspicious anomalies. Currently, their advanced visual analytics systems can achieve 99% accuracy and with 21.4% of market share for CCTV and Video Surveillance Equipment worldwide, Hikvision has clearly secured a respectable position in the video surveillance space.

Predict crime locales – Predpol

Predicting future crime spots is no mean feat! But Predpol is proud to venture into that nuanced area with their powerful big data and machine learning capabilities that can predict the time and location new crimes are most likely to happen. And that can be done through data analysis of past crimes. Historical data plays an integral part in building such algorithms.

Los Angeles is one of the American cities that have adopted their system, among others.

Who commits the crime – Cloud Walk

Cloud Walk, the Chinese facial recognition enterprise is foraying into a new scope of technology where it would be possible to predict if a person decides to commit a crime, even before he attempts to. As a result, they have built a system to detect suspicions changes in the manner or behavior of an individual. For example, if a person buys a hammer, that’s completely fine. But of course, if he buys a knife and a rope, he comes under the radar of suspicion.

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Find suspects most likely to commit another crime – Hart

If you know, the individuals charged of a crime are soon released until they stand trials. Now, deciding who should be released pre-trial is like being in deep water. For that, Durham, UK has employed AI technology to enhance their current system of deciding which suspect to release. The program is called Harm Assessment Risk Tool (Hart), and is fed with 5 years’ worth of criminal data for smoother prediction of a person’s vulnerability towards crime.

A whole body of data is used to predict whether an individual falls under the purview of low, medium or high risk. Comparing the prediction with the real world results, we found out that most of the predictions of HART were close to being accurate.

The robust growth of AI and machine learning is the best thing since sliced bread. Their superior technology for crime detection is already in place, and is growing to expand further in the future.

Keeping that in mind, we at DexLab Analytics offer a bunch of Machine Learning Using Python courses to shape your future for good. Our Machine Learning Courses are of top quality and fits the budget of all.

The article has been sourced from – https://www.techemergence.com/ai-crime-prevention-5-current-applications/

 

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

A Change Is In the Make – due to increasing competition among tech companies working on AI, several software makers are inventing their own new hardware. A few Google servers also include chips designed for machine learning, known as TPUs exclusively developed in-house to ensure higher power and better efficiency. Google rents them out to its cloud-computing consumers. Of late, Facebook too shared its interest in designing similar chips for its own data centers.

However, a big player in AI world, Microsoft is skeptical if the money spent is for good – it says the technology of machine learning is transforming so rapidly that it makes little sense to spend millions of dollars into developing silicon chips, which could soon become obsolete. Instead, Microsoft professionals are pitching for the idea of implementing AI-inspired projects, named FPGAs, which can be re-modified or reprogrammed to support latest forms of software developments in the technology domain.  The company is buying FPGAs from chip mogul, Intel, and already a few companies have started buying this very idea of Microsoft.

This week, Microsoft is back in action with the launch of a new cloud service for image-recognition projects, known as Project Brainwave. Powered by the very FPGA technology, it’s one of the first applications that Nestle health division is set to use to analyze the acuteness of acne, from images submitted by the patients. The specialty of Project Brainwave is the manner in which the images are processed – the process is quick as well as very low in cost than other graphic chip technologies used today.

It’s been said, customers using Project Brainwave are able to process a million images in just 1.8 milliseconds using a normal image recognition model for a mere 21 cents. Yes! You heard it right. Even the company claims that it performs better than it’s tailing rivals in cloud service, but unless the outsiders get a chance to test the new technology head-to-head against the other options, nothing concrete can be said about Microsoft’s technology. The biggest competitors of Microsoft in cloud-service platform include Google’s TPUs and graphic chips from Nvidia.

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At this stage, it’s also unclear how widely Brainwave is applicable in reality – FPGAs are yet to be used in cloud computing on a wide scale, hence most companies lack the expertise to program them. On the other hand, Nvidia is not sitting quietly while its contemporaries are break opening newer ideas in machine learning domain. The recent upgrades from the company lead us to a whole new world of specialized AI chips that would be more powerful than former graphic chips.

Latest reports also confirm that Google’s TPUs exhibited similar robust performance similar to Nvidia’s cutting edge chips for image recognition task, backed by cost benefits. The software running on TPUs is both faster and cheaper as compared to Nvidia chips.

In conclusion, companies are deploying machine learning technology in all areas of life, and the competition to invent better AI algorithms is likely to intensify manifold. In the coming days, several notable companies, big or small are expected to follow the footsteps of Microsoft.

For more machine learning related stories and feeds, follow DexLab Analytics. It is the best data analytics training institute in Gurgaon offering state of the art machine learning using python courses.

The article has been sourced from – https://www.wired.com/story/microsoft-charts-its-own-path-on-artificial-intelligence

 

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

  • Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

  • Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

  • Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product.  It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

  • Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

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Advantages of using Python Machine Learning:

  • Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
  • Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
  • Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
  • Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

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