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Category: Machine Learning Courses

How Artificial Intelligence is Boosting the Design of Smarter Bionics

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How Artificial Intelligence is Boosting the Design of Smarter Bionics

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.

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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.

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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.

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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.

For more such interesting blogs follow Dexlab Analytics. Also take a look at the Machine Learning courses being offered by Dexlab Analytics– a premier analytics training institute in Gurgaon.

 

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

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

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

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.

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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.

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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?

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

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.

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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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Quantum Computing Going Commercial: IBM and Google Leading the Trail

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Quantum computing is all set to make a debut in the commercial world – tech bigwigs, like IBM and Google are making an attempt to commercialize quantum computing. Julian Kelly, a top notch research scientist at Google’s Quantum AI Lab announced with a joint collaboration with Bristlecone, a quantum processor that offers a testbed for various research activities on quantum technology and machine learning, quantum supremacy can be achieved and this could be a great stepping stone for building larger scale quantum computers.

QUANTUM COMPUTING GOING COMMERCIAL: IBM AND GOOGLE LEADING THE TRAIL

After Google, IBM is also making significant progress in commercializing quantum computing technology by taking it to the cloud in 2016 with a 5 qubit quantum computer. Also, last year, November they raised the bar by declaring that they are going to launch third generation quantum computer equipped with a 50 quibit prototype, but they were not sure if it will be launched on commercial platforms, as well. However, they created another 20 qubit system available on its cloud computing platform.  

Reasons Behind Making Quantum Computing Commercialized:

Might lead to fourth industrial revolution

Quantum computing has seeped in to an engineering development phase from just a mere theoretical research – with significant technological power and constant R&D efforts it can develop the ability to trigger a fourth industrial revolution.

Beyond classic computing technology

Areas where conventional computers fail to work, quantum computing will instill a profound impact – such as in industrial processes where innovative steps in machine learning or novel cryptography are involved.

Higher revenue

Revenues from quantum computing are expected to increase from US$1.9 billion in 2023 to US$8.0 billion by 2027 – as forecasted by Communications Industry Researchers (CIR).

Market expansion

The scopes of quantum computing have broadened beyond expectations – it has expanded to drug discovery, health care, power and energy, financial services and aerospace industry.

From cloud to on-premise quantum technology

To incorporate quantum computing into the heart of the business operations’ computing strategy, the companies are contemplating to add a new stream of revenue by implementing quantum computing via cloud. In the future, it’s expected to see a rise in on-premise quantum computing – because the technology is already gaining a lot of accolades.

Better growth forecasts

In the current scenario, the quantum enterprise market is still at a nascent stage with a large user base in the R&D space. But by 2024, it has been forecasted that this share would be somewhere around 30% and the powerful revenue drivers will be industries, like defense, banking, aerospace, pharmaceutical and chemical.

IBM or Google? Who is a clear winner?

In the race to win quantum supremacy, IBM is a sure winner and has made stunning progress in this arena, even though it is receiving stiff competition by Google recently. Google’s new quantum processor Bristlecone has the ability to become a “compelling proof-of-principle for building larger scale quantum computers”. For this, Julian Kelly suggested, “operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself. Getting this right requires careful systems engineering over several iterations.”

 

As last notes, quantum computing has come out from being a fundamental scientific research to a structural engineering concept. Follow a full-stack approach, coupled with rapid testing and innovative practices and establish winning control over this future tool of success.

In this endeavor, DexLab Analytics can for sure be of help! Their business analytics certification online courses are mindblowing. They also offer machine learning using python courses and market risk training – all of them are student-friendly and prepared after thorough research and fact-finding.

 

The article has been sourced from – https://analyticsindiamag.com/why-are-big-tech-giants-like-google-ibm-rushing-to-commercialize-quantum-computing

 

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How Algorithms Shape Public Discourse and Opinions?

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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.

 
How Algorithms Shape Public Discourse and Opinions?
 

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

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

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.

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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.

To grasp emerging trends, newer solutions, robust techniques and real-life case studies, take up Machine Learning Using Python courses from DexLab Analytics. Their Machine Learning Training Gurgaon simply gives an out of the world experience, thus need to be tried on.

 

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part II)

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This blog is a continuation of the previous blog that talked about how data science is improving retail – through cutting edge machine learning training models. For sure, retail and ecommerce churns out a humongous amount of data, but with no proper tool of analysis, the vast pool of data lies unutilized, unearthed and you end up knowing what you already know.

How Machine Learning Coupled With Data Science Improves Retail Scenario (Part II)

In this blog, we will delve into a few common uses of data science in retail – which demands absolute attention, before we start automating procedures.

Product Recommendations

In a traditional shop setup, retailers would have consultants who would understand customer requirements and use their own judgments to help them find a suitable product. In an online scenario, this would be based on past performance and revenue generation and the sole aim would be to pitch the highest selling product. Not only historical purchases and recent online activity, but consumer’s social media and online sharing would give more idea about their interests, preferences and designer they like.

Sometimes, we also come across ghost clients – clients about whom we have no information, in fact, we don’t even know from where they are browsing. In this case, your recommendations would be based on intuition and might not be 100% accurate.  The deal becomes trickier here. On the other hand, there are clients about whom who know everything – and thus tailor our offerings.

Product Assortment

No wonder, we have to keep products to satisfy our niche customers, but on a wider scale, we have to introspect what stuffs to keep in stock. A proper analysis of our product demands and the kinds of products our clients swear by, we can ascertain what items to restock again and again. Also, we can take a cue or inspiration from our vying competitors, as they are a good source of information for a perfect assortment of products you want to include. A full account of their inventory will enlighten you about a few blind spots you had, and devise how to correct them before it’s too late. 

Pricing

The people will pay whatever rates the market supports. The price of the product is still subject to change, depending on the country of origin, taste and preferences and market scenario. But these are more of a supply side changes, so what about the demand side? Interested customers are keen to buy products even at varying prices, but the products should be truly good enough. The scale also plays an important role in deciding prices. The best pricing decisions take into account data regarding weather, day of purchase, several economic factors, location and more.

Customized branding/marketing

It is mostly curated for large retailers. For example, how about some doing some routine advertising – it applies to both digital and offline branding, though much easier for digital. A monthly newsletter carrying all the needful information about discounts, new product launch and promotions always will keep your customers’ updated about everything that’s going around the company. But, make sure they have some sort of personal touch – personalized marketing helps!

Summary

While the sky is the limit for data science, the blog above sheds light on the benefits of data science and the true impact of having trust on data. After all, it is of no use to keep data and not take advantage of it!

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DexLab Analytics offers premium data science training in Delhi NCR for affordable rates. Try their courses today!

 

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part 1)

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The mammoth growth in ecommerce signifies an entire paradigm shift in retail sector. Figures say, ecommerce accounts for $2 trillion dollars in sales and more. Though traversing through both the offline and online market seems a rather challenging task, but when we finally concentrate on each customer and their purchasing manner, it feels easier to break up the analysis into a few different paths.

How Machine Learning Coupled With Data Science Improves Retail Scenario (Part 1)

In this blog, we will take into account a few interesting ways, in which data science increases your sales, online and offline, alike. But before that, understand whom are you selling your products? Hoarding information about your clients is crucial, and of course there are many ways to do this.  Amazon is one of the biggest examples of this. They predict future purchases of customers, based on the past behavior. Companies lose valuable customers if they don’t look at the data with a wider scope and search for insights. But Amazon is definitely not one of them, and their technique is clearly working for them with over $2 billion profits made last year.

The Mechanism Behind

At Amazon, products are shipped even before customers have ordered them. This means, when the products are shipped, there’s no one to receive them. But, does it really matter! The main logic behind such steps is that once the products are taken out of the warehouse and transported to a particular area, they can easily be marketed to other dealers at discounted rates or kept inside the final hub. This is more like a logistic marvel than an ecommerce miracle – but it definitely makes us believe in the concept of forward thinking to lead the change.

 

amazon-distribution

 

The working principle in here is the most innovative concept of machine learning that helps in predicting future client behavior pattern. It works on data to train a formidable model. Training is a notable process of pouring data into the model so that it can employ statistical weights to automatically identify future purchase trends. For example, Mr. A purchases a new item every two or three weeks, so it’s expected that he will make a purchase within that time limit. For this, we don’t have to use data, but just divide it into train and test data. However, this is a very simple example – in reality these trends are juxtaposed with other millions of clients to differentiate clients into numerous cohorts that overlap and vary. Machine learning techniques are used in a plethora of different use cases, like product recommendations, churn predictions, logistics planning and automatic personalized marketing. We will discuss deeply about them in our next blog section.

 

supervised-machine-learning

 

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Make Flexibility Your Bae

While working on data science, it is important to focus on flexibility – the whole structure of data warehouse will start changing once you start trying something new. At times it may seem to be amusing, but on the long run, you will come across several significant insights.

 

 

With all these on point, scoring high on retail is no more a distant dream. Data science and machine learning methods have made everything so easy, and so manageable. To give a robust push to your career in data science, take up data science online training from DexLab Analytics. Apart from data science, they also offer excellent Machine Learning Certification for all data-hungry candidates – go take a look at their course structure.

 

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Getting Started with Machine Learning: Crack the Code

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Getting Started with Machine Learning: Crack the Code

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.

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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.

Get amazing Machine Learning course online only at DexLab Analytics. Being an incredible online training platform for data science, they offer the best machine learning training at affordable prices in Delhi NCR.

 

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