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Category: Machine Learning Courses

Humans and Automation Shares an Everlasting Bond for a Successful Tech Future

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Humans and Automation Shares an Everlasting Bond for a Successful Tech Future

“God created the world in seven days, because he didn’t have to port anything from legacy systems” – the CEO of a blue chip IT company once quoted. A similar idea was even echoed by MIT’s former director of computer science and AI, Mr. Rodney Brooks who penned down an article “Seven Deadly sins of AI Predictions,” which largely focused on the rate of deployment and the influence of technology over it.

But for any technological revamp, humans are the key ingredient for successful implementation of AI – because they are the ones who have invented such striking tools of automation with their own wit and determination. AI has enhanced productivity, coupled with raising standards of living. Companies all across the globe are recognizing the benefits of AI, and contemplating investments in this budding field of science to trigger greater competitiveness.

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According to research, there exists a potent relationship between degree of automation and profit generation –the companies that have automated their business processes get to enjoy the perks of higher revenue growth six times more than those who didn’t. Also, they are twice more likely to supersede their pre-determined financial goals.

Now coming to our chief area of concern, how humans deliver a significant impact in coordinating automation with AI projects – their process of imagination, understanding, leadership quality, emotional intelligence and versatile management skills outweighs the very fundamentals of technology, hence it is said that for successful digital transformation, investment on human workforce is indispensable. To derive the best results, it is important to shell out money on crucial human elements that will lead to fuller automation and successful AI implementation.

Automation makes people more human. It liberates them from doing humdrum, repetitive work that pulls them back from doing something productive, or creative. Without AI, businesses can’t work or obtain competitive advantage in the future, making them defenseless. Nevertheless, you can’t expect AI to do a whole bunch of things for you, jobs that require creativity, empathy, critical thinking, leadership, artistic expression are meant for humans, and no automation will be able to fulfill those qualities. Humans are the meat and potatoes for AI, and we can’t agree more!

For better successful ventures, it is imperative to make humans and machines work together – it will only make us better in our job profiles. Also, this kind of relationships best augments the deep-rooted potentials of human beings, making humans more humane.

Research also says in the coming days, creative human skills will garner even more demand. Comprehensive training and skill development is highly advisable to remain ahead in the rat-race of advanced technology. Skills like, creativity, emotional intelligence, collaboration, critical thinking, communication and cognitive flexibility will become key skills to grab specific job titles. 

An advice to make: before entering the workforce, it is better seek broad educational experiences in the field of data science or computer science, or your preferred field of study, and then amp up your CV with a professional, program-centric Machine Learning training Delhi. In this way, you will be always updated and stay ahead of the curve.

 

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Bad Data is Really Bad for Machine Learning: Here’s Some Ways to Fix It

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Bad Data is Really Bad for Machine Learning: Here’s Some Ways to Fix It

The quality of data is the talisman of decision-making. Irrespective of the goals, the key to better decision-making lies in the quality of data. As it’s said, bad data takes its toll on organization’s data endeavors – as a result, only 25% of businesses are able to optimize the use of data for revenue generation, despite a volley of resources being thrown at them.

IBM has reckoned that bad data costs companies some $3.1 billion a year in the US alone, while as per Experian’s Data Quality survey, 83% of organizations alleged their revenue is affected by imprecise and incomplete customer or prospect data.

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How to Leverage AI Strategy in Business?

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How to Leverage AI Strategy in Business?

Everyday some company or the other are deploying AI into their systems – whether its Spotify’s machine learning program or Bank of America’s chatbot Erica – it seems AI has broken the shackles and left the machine room to enter the mainstream business.

Today’s AI algorithms are framed on remarkably factual machine sight, speech and hearing, and they have easy access to global cache of information. Thanks to Deep Learning, meteoric growth in data and other cutting edge AI techniques, AI performance is staggeringly improving. With these developments, it may seem possible for CIOs, enterprise architects, application managers who are still in nascent stage in gaining expertise in AI to feel like they are lagging behind somewhere. Contrarily, they are doing well for themselves.

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How?

No second thoughts, a majority of data architects are still learning AI technology so as to develop their adoption strategy. AI is an ever-evolving technology – constant new developments and breakthroughs are emerging out every day, hence crafting a particular strategy might be difficult at times. Luckily, tech oracles like Whit Andrews, VP distinguished analyst, Gartner, are able to pin down distinct trends that determines the direction of AI in the business, while leveraging its capabilities to the fullest.

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Check out these three trends that Andrews focuses on to develop formidable AI strategy for your business setup:

Data Science and Machine Learning: In What State They Are To Be Found? – @Dexlabanalytics.

AI will mushroom normal, contextual user-machine interfaces

Google Home and Amazon Echo have penetrated the homes of thousands, taking the consumer space by a storm. Human-computer interaction is now shifting its base from tactile touchscreens and keyboards to voice – the voice recognition is not only limited to distinct commands but deciphers normal human speech.

Natural language processing (NLP) is the reason behind such intrinsic advancements – and we can’t thank more! NLP and natural language generation have improved operations. The workers employed in parts of Eastern Europe can now talk to their system in their own language and grasp the things that need to be done to complete their designated work, making the whole system work seamlessly.

Incredible Tech Transformation: How Machine Learning is changing the Scope of Business – @Dexlabanalytics.

IoT is the future of AI and Fluid Application Integration

IoT devices gather data from the real world, exchange the data, and perform tasks sent through the internet. In general, they are simple in make but when combined with AI, they can rock the world. How would it be if you find an AI-powered IoT that receive orders, grab products and pack them in containers to be shipped across! Impressive, right?

Besides, AI works upon boosting existing organization applications. AI is like a magical stone that improves customer engagement and support, and Bank of America’s chatbot Erica is a perfect example of that.

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

A complex computing ecosystem will surface out with AI at the center

While companies diversify their systems, computing ecosystem strives to be the beacon of hope – it includes an intricate mix of customers, staffs, IoT devices, applications and data, coupled with AI in the nucleus. This will ensure:

  • Better interaction between people and devices
  • Proper communication between applications
  • And everything in between

No wonder, such ecosystems presents organizations more integrated automation, deeper insight, and better customer experience. Moreover, Gartner has predicted that more virtual agents will get involved in a majority of business interactions between organizations and individuals by 2020 – so the rise of machines is here, and we are extremely excited about it!

Help develop a well-devised AI strategy – with DexLab Analytics. Our consultants will feed you meaningful information on everything related to AI and machine learning. Our machine learning training course is impressive, and if you want to excel in machine learning training, drop by DexLab Analytics. We have a lot of things in store for you!

 

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5 Hottest Online Applications Inspired by Artificial Intelligence

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5 Hottest Online Applications Inspired by Artificial Intelligence

Artificial Intelligence projects, applications and platforms are being churned out from every corner of the world. A majority of them now possess the ability to break loose lab life and hit mainstream trends, making an appearance in myriad online tools, open source APIs and mass gadgets.

Though the machines are yet to take over our lives, they are filtrating their way into our lives, influencing day-to-day activities, be it work or entertainment. From personal assistants like Alexa and Siri, to self-driving vehicles powered by predictive modeling and more intense and fundamental machine learning technologies, a wide set of applications of AI are in use of late.

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We perused through a handful number of AI apps so that we can enlist the ones that are more practical and thus really deserving! Let’s leverage piles of data with these effective applications:

Siri

As per Creative Strategies report, 70% of iPhone users have used Siri at least for once or sometimes, but everyone has tried it at least. We are here to tell you don’t hire a personal assistant, instead implement Siri.

siri

This voice-powered virtual assistant makes business operations smoother and hassle-free, while making your workday more productive. The software is activated by voice, and it is at present available in 20 languages.

Alexa

Developed and powered by Amazon for Amazon Echo intelligent speaker, Alexa, a robust voice service was launched in 2014. It can help you in ordering supplies, translating and controlling office’s vacuum.

amazon-echo

However, connecting your Echo to IFTTT may allow you to coordinate with services that aren’t supported originally by the Echo, while allowing you to integrate multiple actions into a single command to the Echo.

Google Now

This is one of the most popular artificial intelligence applications. Google Now functions by keeping a tab on your calendar, mail, web searches and lot more, along with sending relevant alerts and news on your device as and when detected. It can also carry out tasks, and answer queries, based on voice commands.

google-now

The best part of this application is that you don’t have to log in to use it. Just set up alerts that will be sent to the device, and that’s all. At present, it is available in English and is considered a tailing rival of Siri.

Cortana

If you know the exact way to maneuver it, Cortana would be the most effective AI personal assistant. It can perform all sorts of things, right from dictating and sending emails, tracking flights to searching something on the internet or checking weather forecasts. The more time you spent on it, its functionality gets better and better.

cortana

Even, the company is so impressed by its services that it has integrated the service into Power BI, its most intuitive BI tool.

Braina

Brain Artificial, aka Braina is self-regulating software, which enables easy hands-free operation in your computer to perform basic tasks by listening to voice based commands in English language.

braina-1

Braina enjoys a certain edger over its run of the mill competitors as it can precisely work with a variety of accents, which is not so common. The pro version is equipped with a bonus of deep learning – it is programmable as well as observes user behavior over time.

Hope, AI applications serves the humanity well!

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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

A lot of myths are going around about Artificial Intelligence.

In a recent interview, Alibaba founder Jack Ma said AI can pose a massive threat to jobs around the world, along with triggering World War III. The logic of shared by him explained that in 30 years, humans will be working for only 4 hours a day, and 4 days a week.

Fuelling this, Recode founder Kara Swisher vouched for Ma’s prediction. She supported him by saying Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.” 

Besides, I find all these stuffs quite baffling. I think that if AI is going to be the driving force towards innovation and bringing in a new technological revolution, it’s upon US to curate the opportunities that will require new jobs. Apocalyptic predictions just don’t help.

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Let’s highlight the myths and the logical equations:

Myth 1: AI is going to kill our jobs – it can never happen

Remember, it’s humans who have created robots. We excel at mechanizing, systematizing and automating. We spurred the automation drive, while infusing intelligence to the machines.

The present objective is to create AIs that can work together with human intelligence to develop new narratives for problems we are yet to solve. To solve these new problems, we need new kinds of jobs – there’s a great scope of opportunity, let’s not believe that AI will kill our jobs.

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Myth 2: Robots are AI. Not at all.

From drones to self-organizing shelves in warehouses to machines sent to Mars, all are just machines programmed to function.

Myth 3: Big Data and Analytics are AI. Who said that?

Data mining, Data Science, Pattern Recognition – they are just human-created models. They might be intricate or complicated in nature, but not AI. Data and AI are two entirely different and divergent concepts.

Myth 4: Machine Learning and Deep Learning are AI. Again a big NO.

Though Machine Learning and Deep Learning are a part of the enormous AI tool kit, they are not AI. They are just mere tools to program computers to tackle complex patterns- like the way your email filters out spam by “understanding” what hundreds and thousands of users have identified as spam. They look uber smart, undeniably, in fact scary at times, when a computer wins against a renowned expert at the game GO, but they are definitely not AI.

Myth 5: AI includes Search Engines. Definitely NO.

Search Engines have made our lives easier, undoubtedly. The way you can search information now was impossible few years back, but being the searcher, you too contribute the intelligence. All the computer does is identify patterns from what you search and suggest it to others. From a macro perspective, it doesn’t actually know what it finds because it’s dumb in the end. We feed them intelligence, otherwise they are nothing.  

So, instead of panicking about the uncertainties that AI may bring into our lives, we should take a bow and appreciate the efforts humans gave into creating something so huge, so complex like AI.

And remember, AI has always created jobs in the past and didn’t take them. So, be hopeful!

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5 New-Age IT Skill Sets to Fetch Bigger Paychecks in 2017

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Technology is the king. It is slowly intensifying its presence over workplaces, and is one of the chief reasons why companies are laying off employees. Adoption of cutting-edge technologies is believed to be the main reason of job cuts and by now if professional techies are not properly equipped with newer technologies under their sleeves, the future of human workforce seems bleaker.

 
5 New-Age IT Skill Sets to Fetch Bigger Paychecks in 2017
 

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A recent report says – India would lose about 69,000 jobs until 2021 due to the adoption of IoT, so do you really think human intelligence is losing its intellect? Will AI finally surpass brain power?

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The Evolution of Neural Networks

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The Evolution of Neural Networks

Recently, Deep Learning has gone up from just being a niche field to mainstream. Over time, its popularity has skyrocketed; it has established its position in conquering Go, learning autonomous driving, diagnosing skin cancer, autism and becoming a master art forger.

Before delving into the nuances of neural networks, it is important to learn the story of its evolution, how it came into limelight and got re-branded as Deep Learning.

The Timeline:

Warren S. McCulloch and Walter Pitts (1943): “A Logical Calculus of the Ideas Immanent in Nervous Activity”

Here, in this paper, McCulloch (neuroscientist) and Pitts (logician) tried to infer the mechanisms of the brain, producing extremely complicated patterns using numerous interconnected basic brain cells (neurons).  Accordingly, they developed a computer-programmed neural model, known as McCulloch and Pitt’s model of a neuron (MCP), based on mathematics and algorithms called threshold logic.

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Marvin Minsky (1952) in his technical report: “A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement”

Being a graduate student at Harvard University Psychological Laboratories, Minsky executed the SNARC (Stochastic Neural Analog Reinforcement Calculator). It is possibly the first artificial self-learning machine (artificial neural network), and probably the first in the field of Artificial Intelligence.

Marvin Minsky & Seymour Papert (1969): “Perceptron’s – An Introduction to Computational Geometry” (seminal book):  

In this research paper, the highlight has been the elucidation of the boundaries of a Perceptron. It is believed to have helped usher into the AI Winters – a time period of hype for AI, in which funds and publications got frozen.

Kunihiko Fukushima (1980) – “Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position” (this concept is an important component for Convolutional Neural Network – LeNet)

Fukushima conceptualized a whole new, much improved neural network model, known as ‘Neocognitron’. This name is derived from ‘Cognitron’, which is a self-organizing multi layered neural network model proposed by [Fukushima 1975].

David B. Parker (April 1985 & October 1985) in his technical report and invention report – “Learning – Logic”

David B. Parker reinvented Backpropagation, by giving it a new name ‘Learning Logic’. He even reported it in his technical report as well as filed an invention report.

Yann Le Cun (1988) – “A Theoretical Framework for Back-Propagation”

You can derive back-propagation through numerous ways; the simplest way is explained in Rumelhart et al. 1986. On the other hand, in Yann Le Cun 1986, you will find an alternative deviation, which mainly uses local criteria to be minimized locally.

 

J.S. Denker, W.R. Garner, H.P. Graf, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, H.S. Baird, and I. Guyon at AT&T Bell Laboratories (1989): “Neural Network Recognizer for Hand-Written ZIP Code Digits”

In this paper, you will find how a system ascertains hand-printed digits, through a combination of neural-net methods and traditional techniques. The recognition of handwritten digits is of crucial notability and of immense theoretical interest. Though the job was comparatively complicated, the results obtained are on the positive side.

Yann Le Cun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel at AT&T Bell Laboratories (1989): “Backpropagation Applied to Handwritten ZIP Code Recognition”

A very important real-world application of backpropagation (handwritten digit recognition) has been addressed in this report. Significantly, it took into account the practical need for a chief modification of neural nets to enhance modern deep learning.

Besides Deep Learning, there are other kinds of architectures, like Deep Belief Networks, Recurrent Neural Networks and Generative Adversarial Networks etc., which can be discussed later.

For comprehensive Machine Learning training Gurgaon, reach us at DexLab Analytics. We are a pioneering data science online training platform in India, bringing advanced machine learning courses to the masses.

 

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Indian Startups Relying on Artificial Intelligence to Know Their Customer’s Better

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Indian-Startups-Relying-on-Artificial-Intelligence-to-Know-Their-Customers-Better

Artificial Intelligence was there decades ago, but everyone is talking about AI and Big Data in India’s startup ecosystem of late.

Budding startups are looking for new talent with AI expertise to inspect and evaluate consumer data and provide customized services to the users. At the same time, tech honchos such as Apple have discovered the huge potentials hidden within Indian companies that help their clients with data processing, image and voice recognition, and no wonders, investors are too hopeful for Indian AI startups.

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Here are a slew of Indian unicorns – companies valued at $1 billion or more that are putting in use the exploding technology of AI in the best way possible:

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Paytm

An eye-piercing transformation from being an e-wallet to selling flight or movie tickets, Paytm is now implementing machine learning to bring order into chaos. The company’s chief technology offer, Charumitra Pujari, said, “You could Google and try to look for something. But a better world would be when Google could on its own figure out Charu is looking for ‘x’ at this time. That’s exactly what we’re doing at Paytm,” he further added, “If you’ve come to buy a flight ticket, because I understand your purchase cycle, I show that instead of a movie ticket or transactions.”

In order to identify and prevent fraudulent activities, machines are constantly assessing illicit accounts that purposefully sign up to derive advantage of promo codes, or for money laundering intention. The fraud-detection engine is extremely efficient, leaving no room for human error, Pujari stated.

The team at Paytm is versatile – machine learning engineers, software engineers, and data scientists are in action in Toronto, Canada, as well as in Paytm’s headquarters in Noida, India. Currently, they have 60 people working for them in each location – “We know the future is AI and we will need a lot more people,” said Pujari.

Ola cabs

One of the most successful ride-hailing apps in India, Ola uses machine learning tech to track traffic, crack through driver habits, improve customer experience and enhance the life of each vehicle they acquired. AI plays a consequential role in interpreting day-in-day-out variations in demand and to decipher how much supply is required to cater to its increased demand, how variable are traffic predictions and how rainfall affects the productiveness of vehicles.

olacabs-picture

“AI is understanding what is the behavioral profile of a driver partner and, hence, in which way can we train him to be a better driver partner on (the) platform,” co-founder and chief technology officer Ankit Bhati said, the algorithms put into the car-pooling service works great in pulling down travel times by coordinating with various pick-up points and destinations, while sharing one single vehicle, he further added.

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Flipkart

According to a report in Forbes, Flipkart – India’s largest domestic e-commerce player has already re-designed its app’s home screen to give a more personalized version of services to its mushrooming 120 million patrons. Machine learning models crack each customer’s gender, brand preference, store affinity, price range, volume of purchases and more. In fact, in future, the company is going forward to figure out the reasons about when and why the returns are made, and as a result will try to reduce their happenings. 

Flipkart

A squad of 25 data scientists at Flipkart have started using AI to observe the past buyer behavior to predict their future purchases. “If a customer keys in a query for running shoes, we show only the category landing pages of the particular brand the customer wants to see, in the price point and styles that (are) preferred, as gauged by previous buying behaviour, therefore ensuring a faster, smoother checkout process,” Ram Papatla, the vice president of product management at Flipkart, said recently at an interview with a leading daily.

ShopClues, InMobi, SigTuple and EdGE Network are myriad other Indian startup players who are making it really big by utilizing the powerful tentacles of AI and machine learning.

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Role of Self Service Analytics in Businesses

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Role of Self Service Analytics in Businesses

Self Service Analytics is proving useful for business users, who are working on business data without necessarily having a background in technology and statistics. It is essentially bridging the gap between trained data analysts and normal business users.

Following are the characteristics of Self Service Analytics:

  1. Business Users Independence:

Self Service Analytics reduces dependency on IT and Data warehousing teams, thereby reducing the turnaround time for a request made by a business user.

It does so by continuously collating and loading real time data into a singular stream without disparity, which is easily accessible through browsers. Thus, it helps business users in taking decisions on Real-Time basis.

This feature benefits organizations because vital decisions made within time can be more profitable as compared to the traditional way of analysing data, which may not be a good idea in respect to the urgency constraint.

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  1. Easier and Reduced Cost of Operations:

Often, the company’s data are fragmented and widespread across various divisions. This increases the headache of channelling the data meaningfully and in a wholesome manner.

Further to this, preparing reports using this data becomes a cumbersome job for the IT department or the department, which is serving such request. Hence, it may lead to increased cost of time or decreased quality of efficiency at which the operations have to run. However, many a times, these reports fail to give an overview of the operations in an organisation.

Self-service BI integrates data from different systems and delivers a “Single Version of Truth”. Accessing this data and running computations on it requires only a browser for access and eliminates the need to install, maintain and administer large-footprint software clients on each user’s workstation.

If Self Service Analytics is hosted on SaaS, it will further reduce the cost of machinery and maintenance associated with it. The provision for usage can be increased or decreased in no time according to the usage pattern. This really means that Self Service Analytics helps you adapt with time and Pay-Per-Use model, which is a leading trend in most of the industries.

  1. Resolving the conflict over accuracy:

Typically, a business user using Excel would have a local copy of data and run computations on it. He can merge and transform it by using various formulas and finally derive a conclusion.

This is dangerous because in live operations, data keeps changing and data integrity is at stake by working on local copies. Thus, accuracy in decision-making becomes a game of luck.

In Self Service BI, the data from the source is extracted, transformed and loaded into a unique data model, which goes with all operations. In this case, data integrity is assured. In addition, all business users have the same source of data, removing the risk that working with different local copies have.

Therefore, from the above stated facts, we can conclude that Self Service Analytics is a need for today’s businesses.

However, there are a few risks involved in Self Service Business Analytics:

  1. Loose corporate governance and make data available to business users directly may be taken advantage of in an undue manner.
  2. Business users may not be properly trained or skilled to make decisions.
  3. Relying heavily on any tool without some real life experience and insight into the background of that data can result into an impaired decision-making.

If all the above-mentioned risks are mitigated and proper corporate governance structure is in place, Self Service Analytics can be very beneficial for the success of any organization.

To excel in Self-Service Analytics, why not take up Machine Learning courses in Delhi from DexLab Analytics! They are informative, interesting and elaborate.





 

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