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Category: Machine Learning Using Python

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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R is Gaining Huge Prominence in Data Analytics: Explained Why

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Why should you learn R?

Just because it is largely popular..

Is this reason enough for you?

Budding data analytics professionals look forward to learn R because they think by grasping R skills, they would be able to nab the core principles of data science: data visualization, machine learning and data manipulation.

Be careful, while selecting a language to learn. The language should be capacious enough to trigger all the above-mentioned areas and more. Being a data scientist, you would need tools to carry out all these tasks, along with having the resources to learn them in the desired language.

In short, fix your attention on process and technique and just not on the syntax – after all, you need to find out ways to discover insight in data, and for that you need to excel over these 3 core skills in data science and FYI – in R, it is easier to master these skills as compared to any other language.

Data Manipulation

As rightly put, more than 80% of work in data science is related to data manipulation. Data wrangling is very common; a regular data scientist spends a significant portion of his time working on data – he arranges data and puts them into a proper shape to boost future operational activities. 

In R, you will find some of the best data management tools – dplyr package in R makes data manipulation easier. Just ‘chain’ the standard dplyr together and see how drastically data manipulation turns out to be simple.

For R programming certification in Delhi, drop by DexLab Analytics.

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Data Visualization

One of the best data visualization tools, ggplot2 helps you get a better grip on syntax, while easing out the way you think about data visualization. Statistical visualizations are rooted in deep structure – they consist of a highly structured framework on which several data visualizations are created. Ggplot2 is also based on this system – learn ggplot2 and discover data visualization in a new way.

However, the moment you combine dplyr and ggplot2 together, through the chaining technology, deciphering new insights about your data becomes a piece of cake.

Machine Learning

For many, machine learning is the most important skill to develop but if you ask me, it takes time to ace it. Professionals, who are in this line of work takes years to fully understand the real workings of machine learning and implement it in the best way possible.

Stronger tools are needed time and often, especially when normal data exploration stops producing good results. R boasts of some of the most innovative tools and resources.

R is gaining popularity. It is becoming the lingua franca for data science, though there are several other high-end language programs, R is the one that is used most widely and extremely reliable. A large number of companies are putting their best bets on R – Digital natives like Google and Facebook both houses a large number of data scientists proficient in R. Revolution Analytics once stated, “R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.” Besides the tech giants, a wide array of medium-scale companies like Uber, Ford, HSBC and Trulia have also started recognizing the growing importance of R.

Now, if you want to learn more programming languages, you are good to go. To be clear, there is no single programming language that would solve all your data related problems, hence it’s better to set your hands in other languages to solve respective problems.

Consider Machine Learning Using Python; next to R, Python is the encompassing multi-purpose programming language all the data scientists should learn. Loaded with incredible visualization tools, machine learning techniques, Python is the second most useful language to learn. Grab a Python certification Gurgaon today from DexLab Analytics. It will surely help your career move!

 

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Incredible Tech Transformation: How Machine Learning is changing the Scope of Business

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Incredible Tech Transformation: How Machine Learning is changing the Scope of Business

Machine Learning coupled with data analytics is modifying the norms of how business handles crucial data. Insights into ML and AI is already reaping benefits in transforming vast pools of data – curated by dexterous data pundits into meaningful, relevant analytic results that would have escaped clumsy human analysis, previously.

Today, the combat weapon of Machine Learning has started to influence the entire business world. While many organizations have grasped the bounties of this hi-tech tool of learning, few are left to fathom how it would affect the way they do business. The automation process is a completely data-driven task – ideal to change enterprises into vendors – by turning lessons learnt into advanced algorithm programs worthy of licensing to software and service providers for good money.

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Nevertheless, a lot of all that depends on how machine learning is going to evolve in the coming five to ten years and what implications it would bring into the hiring or recruitment strategies in the long run. And the best area to start off this discussion is unsupervised machine learning, where intricate frameworks are allotted large datasets and asked to draw patterns without human help to figure out what the software needs. With minimum human interference, the scalability of this mode of ML is the highest.

How to Assess Clustering Tendency: Unsupervised Machine Learning – @Dexlabanalytics.

Supervised or Unsupervised? Which is better?

Supervised ML needs human help to develop large sets of training data and corroborate the results of the training. Speech Recognition is the perfect example of such ML. But it is challenging to procure and classify vast data for supervised training. As a result, unsupervised ML is the key to the future – it reduces such interaction to a large extent. The minimum involvement of human beings suffices to be a boon – but take a note, a data scientist is required to select the data that is to be evaluated.

Unsupervised learning also needs a human touch to assign values to data structures and clusters. Hence, we cannot say for sure they are totally human-error free. Instead, we should focus more to ace up the performance of humans in tackling data for own interests.

In this context, “I think, right now, that people are jumping to automation when they should be focused on augmenting their existing decision process,” says David Dittman, director of business intelligence and analytics services at Procter & Gamble. “Five years from now, we’ll have the proper data assets and then you’ll want more automation and less augmentation. But not yet. Today, there is a lack of usable data for machine learning. It’s not granular enough, not broad enough.”

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

How to become a vendor from a consumer

A portion of what drives an incessant demand for data scientists is the pressing need for data to turn ML more productive. Mike Gualtieri, Forrester Research’s vice president and principal analyst for advanced analytics and machine learning thinks that some organizations, exactly five years from now might turn into vendors -“Boeing may decide to be that provider of domain-specific machine learning and sell [those modules] to suppliers who could then become customers,” he says. Like him, Dittman also sees the thriving combination of Data and ML code as being a highly sellable product, more so a potent new source of revenue for organizations – “Companies are going to start monetizing their data,” he explains. “The data industry is going to explode. Data is absolutely exploding, but there is a lack of a data strategy. Getting the right data that you need for your business case, that tends to be the challenge.”

Irrespective of what the future holds, technology is grooming to become an extravagant revolving door of striking innovation, and the only way to nab this technology is by making ourselves technology-friendly. For excellent business analytics course in Delhi, DexLab Analytics provides the perfect platform to deliver student-friendly education on data analytics at affordable prices. Dig into our data analyst course by clicking on our homepage.

 

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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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Facebook Shut Down AI amid Fears of Losing Control

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Facebook Shut Down AI amid Fears of Losing Control
 

Analysts at Facebook promptly shut down the Artificial Intelligence system over concerns they might lose control over the system. Recently, Facebook had developed a new Artificial Intelligence program, which could create its own language with the help of code words to make communication easier and effective. The researchers took it offline, when they understood the language used is no longer English.

 

fb_ai-657x360-702x336

 

Though this isn’t the first time that AIs went a step ahead to take a different route instead of the oh-so-regular training in English language to develop their own more productive language, the recent Facebook incident made us wary about Elon Musk’s warnings about AI. “AI is the rare case where I think we need to be proactive in regulation instead of reactive,” Musk, co-founder, CEO and Product Architect at Tesla once stated at the meet of US National Governors Association. “Because I think by the time we are reactive in AI regulation, it’ll be too late,” he further added.

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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.

For more such interesting feeds on artificial intelligence and machine learning, follow us at DexLab Analytics. We offer India’s best Machine Learning Using Python courses.  

 

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