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

Posted on May 9, 2018May 9, 2018 by Dexlab

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.

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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How is AI Shaping the Indian Job Market?

Posted on May 7, 2018May 23, 2020 by Dexlab

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.

A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

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How Artificial Intelligence is Boosting the Design of Smarter Bionics

Posted on April 10, 2018May 23, 2020 by Dexlab

Artificial Intelligence and Machine Learning are a set of technologies that empower machines and computers to learn and evolve upon their own learning through constant reiteration and consistent data bank upgrades. The entire chain of mechanism stands on recursive experiments and human intervention.

Bionics

Advances in technology have greatly benefited the field of prosthetics in the last few years. Today’s prosthetic limbs are made using space-age materials that provide increased durability and function. In addition, many prosthetics make use of bionic technology. These types of prosthetics are called myoelectric prosthetics.

Myoelectric prosthetics picks up the electrical action potential from the residual muscles in the amputated limb. Upon receiving the action potentials, the prosthetic amplifies the signal using a rechargeable battery. Detected signal can be used further into usable information to drive a control system. Artificial Intelligence helps to identify motion commands for the control of a prosthetic arm by evidence accumulation. It is able to accommodate inter-individual differences and requires little computing time in the pattern recognition. This allows more freedom and doesn’t require the person to perform frequent, strenuous muscle contractions. The inclusion of AI technology in prosthetics has helped thousands of amputees return to daily activities. While technologies that make bionic implants possible are still in infancy stage, many bionic items already exist.

The Bionic Woman

Angel Giuffria is an amputee actress and Ottobock Healthcare’s Brand Champion who has been wearing electromechanical devices since she was four months old. Following are excerpts of an interview with her.

“I wear currently the bebionic 3 small-size hand which sounds like a car. But at this point, that’s where we’re getting with technology. It’s a multi-articulating device. That small-size hand is really amazing… this technology wasn’t available to them previously”

She further added, “..The new designs that look more tech are able to showcase the technology. I’ve really become attached to and I think a lot of other people have really clung onto as well because it just gives off the impression of showing people how capable we are in society now.”

She also spoke about prosthetics like the Michelangelo Hand which is stronger, faster and has multiple hook options. Modern additions to prosthetics such as lights and cameras are added advantages. She describes her hand to be able to do multiple functions like change grip patterns and control wrist movements which enable her to hold small items like keys and credit cards.

I-limb

Bertolt Meyer’s amazing bionic hand controlled via an iPhone app is another glimpse at the advances being made in prosthetics.

In 2009, Meyer, a social psychologist at the University of Zurich was fitted with an i-limb, a state-of-the-art bionic prosthesis developed by a Scottish company, Touch Bionics, which comes with an aluminum chassis and 24 different grip patterns. To select a new suite of gestures, Meyer simply taps an app on his iPhone. He describes his i-limb to be the first, where aesthetics match engineering.

In the world of prosthetics, function is the key. Most amputees are constantly searching for the same level of functionality that they enjoyed before they lost their limb. With the introduction of artificial intelligence in prosthetic limbs, amputees are closer to their goals than ever before. Bionics having access to the relevant databases are capable of learning new things in a programmed manner which improves their performance.

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R Programming, Python or Scala: Which is the Best Big Data Programming Language?

Posted on April 9, 2018April 9, 2018 by Dexlab

For data science and big data, R, Python and Scala are the 3 most important languages to master. It’s a widely-known notion, organizations of varying sizes relies on massive structured and unstructured data to predict trends, patterns and correlations. They are of expectation that such a robust analysis will lead to better business decisions and individual behavior predictions.

In 2017, the adoption of Big Data analytics has spiked up to 53% in companies – says Forbes.

The story of evolution

To start with, big data is just data, after all. The entire game-play depends on its analysis – how well the data is analyzed so as to churn out valuable business intelligence. With years, data burgeoned, and it’s still expanding. The evolution of big data mostly happened because traditional database structures couldn’t cope with such multiplying data – scaling data became an important issue.

For that, here we have some popular big data programming languages. Dive down:

R Programming

R Programming is mainly used for statistical analysis. A set of packages are available for R named Programming with Big Data in R (pbdR), which encourages big data analysis, across multiple systems via R code.

R is robust and flexible; it can be run on almost every OS. To top that, it boasts of excellent graphical capabilities, which comes handy when trying to visualize models, patterns and associations within big data structures.

According to industry standards, the average pay of R Programmers is $115,531 per year.

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Python

Compared to R, Python is more of a general-purpose programming language. Developers adore it, because it’s easy to learn, a huge number of tutorials are available online and is perfect for data analysis, which requires integration with web applications.

Python gives excellent performance and high scalability for a series of complicated data science tasks. It is used with high-in-function big data engines, like Apache Spark through available Python APIs.

Their Machine Learning Using Python courses are of highest quality and extremely student-friendly.

Scala

Last but not the least, Scala is a general-purpose programming language developed mainly to address some of the challenges of Java language. It is used to write Apache Spark cluster computing solution. Hence, Scala has been a popular programming language in the field of data science and big data analysis, in particular.

There was a time when Scala was mandatory to work on Spark, but with the proliferation of many API endpoints approachable with other languages, this problem has been addressed. Nevertheless, it’s still the most significant and popular language for several big data tools, including Finagle. Also Scala houses amazing concurrency support, which parallelizes a whole many processes for huge data sets.

The average annual salary for a data scientist with Scala skills is $102,980.

In the end, you can never go wrong with selecting any one of the big data programming languages. All of them are equally good, productive and easy to excel on. However, Python is probably the best one to start off with.

For more updates or information on big data courses, visit DexLab Analytics.

The original article is here at – http://www.i-programmer.info/news/197-data-mining/11622-top-3-languages-for-big-data-programming.html

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How Machine Learning and Sensors Help Detect Cyber Threats within Power Distribution Networks

Posted on April 5, 2018April 5, 2018 by Dexlab

Machine learning in marriage with cutting edge sensor technology helps alpha geeks detect and assess cyber-physical attacks across power-distribution channels.

Today, losing power and imagining a life without technology sounds unreal. It’s more than just an inconvenience. The truth is we rely on electricity much more than we even do realize. Even though you are not a techie or someone belonging from the IT domain, you still stay dependent on electricity and power. They have become the BASICS.

For this reason or more, power companies have initiated a ‘deep dependency’ concept, Smart Grid – it’s an effective and powerful power-distribution structure. It’s originally a power-line internet that harbors exceptional capabilities within.

Machine Learning and Sensors to Ensure Security to Power Grids

A team of eminent researchers are toiling rigorously to integrate machine-learning algorithms, cybersecurity methodology and commercially-available power-system sensor technology into a security monitoring and analysis framework to support power grids.

The team is at present working on the framework’s architecture for detection of cyber-physical attacks on any power-distribution network. “To do this they are using micro-Phasor Measurement Units (µPMUs) to capture information about the physical state of the power distribution grid,” explains Kathy Kincade, Lawrence Berkeley National Laboratory. “They then combine this data with SCADA (Supervisory Control and Data Acquisition) information to provide real-time feedback about system performance.”

Note: Kathy Kincade published a Lawrence Berkeley National Laboratory press release: Combination of Old and New Yields Novel Power Grid Cybersecurity Tool, which talks elaborately on this issue.

The notion here is to keep a close watch on the physical behavior of the components within a particular electric grid to understand when devices are under attack, how they are manipulated weirdly. These devices act as a redundant set of measurements that offers veritable ways to monitor everything that’s going within a power distribution grid.

One of the researchers, Sean Peisert (Berkeley Labs) articulates the importance of redundant measurements permitted by implementing both µPMU and SCADA devices. He further says, “Individually it might be possible for an attacker to manipulate what is being represented by any single sensor or source of information, which could lead to damage of the power grid. This approach provides the redundancy and therefore resilience in the view that is available to grid operators.”

System redundancy comes with an additional benefit of distinguishing real attacks from false alarms by comparing µPMU measurements to what the device reports.

An Algorithm for Real-time Reporting

The proud researchers formulated an algorithm in 1954 for their machine learning endeavors. The algorithm aids software in identifying if measurements like active power, reactive power and current magnitude are normal or abnormal by discerning robust changes across the physical environment.

The Last Thoughts

Cyber attacks are becoming increasingly widespread. Every other day, you might find some headlines or tech page news surfacing out, intensifying how cyber attacks are plaguing our lives, digitally. Therefore, it’s high time to learn from the pundits how to work on the issue.

As Peisert concludes, “Using high-resolution sensors in the power-distribution grid and a set of machine-learning algorithms that we developed, in conjunction with a simple model of the distribution grid, our work can be deployed by utilities in their distribution grid to detect cyberattacks and other types of failures,” it stresses on the significance of machine learning algorithms to combat such attacks.

The original article first appeared in – https://www.techrepublic.com/article/power-grid-cybersecurity-tool-uses-machine-learning-and-sensors-to-detect-threats

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Here’s Why Automation Is Gaining Accolades All over the World?

Posted on March 28, 2018December 31, 2018 by Dexlab

Over the last few decades and more, if there’s anything that has continuously evolved, surprising us with something new at each turn, it’s TECHNOLOGY. Technological advancements have come to a point where people and businesses have started relying on Automation.

Automation is the technology by which a process is performed entirely by means of various control systems and equipments without human assistance. The purpose is to enhance productivity, deliver faster results and save costs, depending on the industry and the extent to which automation is being applied.

Automation, which is mostly about streamlining the work process, can take place either by means of implementing intelligence into the existing systems or by replacing the same. Some of these systems operate without human interference while others support humans and pave ways for better productivity.

Leveraging Automation for Good

How to introduce automation into the workflow and the procedure of doing that has been a matter of concern for most entrepreneurs. Putting machines to work requires certain considerations. Following are a few smart tips for introducing automation to your business and leveraging the most out of it.

Putting the machines to work for you necessitates you to implement the automation tools gradually. This should always be followed by a close evaluation of the existing process so that it is applied to where streamlining is needed the most.
Automation is an instant success when you start things manually, but sometimes things can take a downturn if you lack enough knowledge of the domain. The switch to automation should be planned ahead with care.
Automation is a change that most people will be reluctant to adopt at least initially. So, it is always better to ensure that there is a solid change management strategy in place to make the technology actually make lives easier and not otherwise.

Automation isn’t something NEW ON THE BLOCK

The very notion of automation has been in the minds of entrepreneurs as well as traditionalists for around centuries. For an example, an elevator that’s been in existence for so many years is an automation tool for mobility. Recently, massive breakthroughs have been achieved in this budding field, and cloud computing is one of them. It has driven the cost of storage solutions really down, while pushing machine learning skills to a new high.

In short, while automation has always been more of a IT tool, it’s now finding its footing in other domains within an organization. “Whether it’s a catalog or an app, it now allows me as a consumer to control things when they’re happening,” says Marc Wilkinson, CTO for Workplace and Mobility at DXC.

Automation and employment crunch

As it’s said, automation isn’t about eliminating jobs instead it’s more about creating efficiencies. If the work done in twice the speed, it would be more efficient and employer will benefit from productivity gains and the employees in the event will hit off high-impact projects.

So, in a sense, automation won’t entirely result in lower employee headcount, though it may affect a certain kind of employment. High-touch occupations will witness limited impact. For an instance, hospitals have to very much human-to-human; they can’t go totally automized.

In case, you are thinking of having a career in automation, get enrol in a good Machine Learning Certification course. DexLab Analytics offers some of the best machine learning training in Delhi, NCR region: go check out the course itinerary.

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How Algorithms Shape Public Discourse and Opinions?

Posted on March 14, 2018May 23, 2020 by Dexlab

The rapid evolution of today’s communication mediums has brought about a radical change in how public opinions are framed and public discourse is conducted. In general, the conventional boundary between public and personal communication has somewhat disappeared.

Incredible global platforms, like Google allow us all gain access to information in a blink of an eye. In order to do so, they use computer algorithms that weigh “relevance”, but sometimes the standards do not correspond to the expectation of the users.

Custom-fit Relevance

Algorithms function distinctly and descriptively. For an instance, the technology alters “relevance” for a user based on what links he or she has clicked in the past. But still, many users think the results are normative (‘higher’ up in Google results). In several cases, Google’s algorithms determine a massive divergence between content quality and “relevance”.

Not only that, owing to their encompassing database, Google and Facebook play a strong role in forming public opinions. 57% of German internet users get access to information about social affairs and politics through Google and other social networks. The researchers from Hamburg-based Hans Bredow Institute quoted in 2016, “the formation of public opinion is no longer conceivable without intermediaries, such as Google and Facebook”.

Keep Engaged

The design elements that Google and other intermediaries use are leading to a structural change in public discourse. Today, publishing is a piece of cake. Anyone can publish anything on the web, but everyone might not find an audience – for the latter, decision-making algorithms are needed. They garner the needed attention. They also determine the relevance of each content piece that goes through various social networks, like Facebook and filter the items that should be displayed for each user. In making an individual’s feed attractive, Facebook runs a detailed analysis and determines which content the user and his or her friends’ likes or prefers to hide. Both signals are important to perform a fairly straightforward analysis.

Moreover, Facebook deploys signals that users have no idea about, such as the amount of time they take to look into a single entry in the feed. In other areas too, algorithmic decision-making plays a crucial role, like offering help in legal matters or assessing where and when the police officers are on duty.

Diversity Rules

To guarantee a diversity of media in the public, make sure the algorithmic decision-making processes that determine relevance are diverse in the same manner. The digital discourse is supported by the robust algorithms that constantly ranks and personalize content.

To instill transparency to algorithmic decision-making methodologies, follow the steps below:

Back external researchers, and open platforms and their impacts
Support diversity among algorithms
Develop and maintain a strong code of ethics among developers
Educate users about the importance of the mechanisms used to influence public opinions

Coupled with strong industry self-regulation and legislative measures, a true and impartial notion of social and political influences on algorithmic ranking is established, which carries the potential to discover and combat dangers early on.

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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

Posted on March 12, 2018December 17, 2018 by Dexlab

Venture capital companies find it hard to pool in interesting investment options – the task is laborious and travel-intensive. But, thanks to machine learning and predictive analytics – they have now started to transform the entire procedure of how an investor builds up a portfolio altogether.

Considering the power of AI’s utility in determining the most fabulous startup investments, InReach Ventures co-founder Roberto Bonanzinga has decided to invest $7 million on respective software that deploys machine learning to identify significant European startups to invest capital into. Following its footsteps, several other VC firms have started doing this, already just to thrive in.

Rightfully so, AI is an incredible tool that is capable enough to filter out all the unnecessary noise and pull up VCs with potential candidates for sound investment. This makes it easier for entrepreneurs to hit the optimal level of funding and appeal to strong VCs.

AI: An Investment Ally

According to a Social Science Research Network Study, there lies an inherent risk with investing on newbie entrepreneurs, and just only 18% tastes success on their feats. Brand new business owners are ambiguous, they need some scrutiny before investment – for that, AI framework is armed with the required tools and information – it can internalize data to easily derive at conclusions and fasten a success rate to a company on the basis of past industry performance, revenue growth, profit ratios and market size.

As a result, entrepreneurs can tweak their pitches and alter company profiles to better tally with AI, and this how they can start:

Get Deeper

Who doesn’t dream of owning a company that’s a market leader?! However, raising such adequate amount of capital becomes the real challenge. The challenge intensifies when budding entrepreneurs need to attract funds.

For such minority-fronted startups, Alice, a formidable AI platform uses data to decide which businesses are worth funding. Entrepreneurs should implement AI platforms, like Alice to take a deeper look into the key metrics to get a larger picture how their startups are staking up to their tailing rivals who received funding and how well they are functioning.

Tracking Investor Trends Helps

Age-old methods of tracking investment trends are things from the past, because AI and machine learning is changing the entire ball-game. A Berlin-based VC firm Fly Venture plans to target European startups in the seed stage and pre-Series A startups and finally closed its first fund at $41 million. It aims to use machine learning to generate deal flow. This type of technology helps entrepreneurs meet the right investors at right time. After keeping a close eye on the market, it’s about time to utilize the AI-sought information to make sure your company is line with what investors are seeking in a veritable startup partner. This will bear more fruits and less frustration.

Never stop evolving

The best thing about AI is that it never stops improving. Constantly, machine learning is on the move – it analyzes information 24/7 so that entrepreneurs gain access to non-stop updates to tweak their businesses, while pitching for investors.

In a nutshell, to have better insights and cleaner access to data, entrepreneurs need to harness the relentless power of AI. The technology isn’t eating away our jobs, instead its bringing a new change in the data-inspired environment. And if you are already working with it, you’ll understand how it’s reshaping and guiding venture capital to startups that AI finds worthwhile.

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Getting Started with Machine Learning: Crack the Code

Posted on February 20, 2018December 22, 2018 by Dexlab

Machine Learning has taken us all to the tipping point from where the entire ballgame of technology and the way we interact with the digital world has started changing and the surge is expected to continue over the next decade. Increasingly, the decisions of the future are going to be made by machines, and we can’t seem to be more excited!!

It’s time to adopt Machine Learning

According to McKinsey reports, AI adoption in the tech sector is at its nascent stage, with few firms implementing it on a large scale. The companies that are yet to deploy it are still in two minds whether they should expect return on investments or not.

Nevertheless, skilled data scientists better be start speeding up the process of implementation of these emerging technologies if they want to stay right on edge ahead of their tailing rivals. Machine Learning is the new in-thing that must be embarked on RN.

And for that, here goes the following tips that will help you ride towards AI success:

Inspect the areas where data science fits into

Leverage data science and Machine Learning within an organization to trigger better optimization and smoother implementation. Imbed data science and machine learning into every department, like HR, marketing, sales and finance. Also, try pairing data scientists with software engineers to build agile models on machine learning, that’s the best way to scale across company operations better.

Treat data as money

Today, data acts as the fuel for an organization. But it can also be treated as money, and diligent data consultants need to manage, protect and obsess over it. Data is powerful but in order to derive the best out of it, it needs to be played well in the hands of experts. And those hands are of data specialists who values data like money.

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Stop hunting down purple squirrels

No wonder, data scientists are individuals with an exceptionally high aptitude in math and statistics; they are skilled in evaluating insights in data. They don’t necessarily have to be software engineers who only know how to write algorithms and curate tech products. Data scientists are much more than that.

Companies often seek unicorn-like aspirants who are ninja software engineers, ace statisticians and master of industry domain, but the sad part is that they look for all these 3 character traits in a single job candidate, which needs to be changed.

Keep an eye on ‘derived data’

If you are thinking of sharing your algorithms with any other person then the chances are high that they will see your data. But companies that are keen on protecting its data should refrain from such activities. Data for informatics companies is like a new currency – they need to be well-guarded and treasured for life!

Educate about the perks of AI

AI is a blessing, for all you tech nerds and gizmo jerks. And accomplished data professionals should look for ways to promote AI and influence friends and co-workers to embrace this new king-some technology. After all, successful machine learning implementation may become the key to your company’s future growth, provided you treat it in the right manner.

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