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AI-Related Tech Jargons You Need To Learn Right Now

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AI-Related Tech Jargons You Need To Learn Right Now

As artificial intelligence gains momentum and becomes more intricate in nature, technological jargons may turn unfamiliar to you. Evolving technologies give birth to a smorgasbord of new terminologies. In this article, we have tried to compile a few of such important terms that are related to AI. Learn, assimilate and flaunt them in your next meeting.

Artificial Neuron Networks – Not just an algorithm, Artificial Neuron Networks is a framework containing different machine learning algorithms that work together and analyzes complex data inputs.

Backpropagation – It refers to a process in artificial neural networks used to discipline deep neural networks. It is widely used to calculate a gradient that is required in calculating weights found across the network.

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Bayesian Programming – Revolving around the Bayes’ Theorem, Bayesian Programming declares the probability of something happening in the future based on past conditions relating to the event.

Analogical Reasoning – Generally, the term analogical indicates non-digital data but when in terms of AI, Analogical Reasoning is the method of drawing conclusions studying the past outcomes. It’s quite similar to stock markets.

Data Mining – It refers to the process of identifying patterns from fairly large data sets with the help statistics, machine learning and database systems in combination.

Decision Tree Learning – Using a decision tree, you can move seamlessly from observing an item to drawing conclusions about the item’s target value. The decision tree is represented as a predictive model, the observation as the branches and the conclusion as the leaves.  

Behavior Informatics (BI) – It is of extreme importance as it helps obtain behavior intelligence and insights.

Case-based Reasoning (CBR) – Generally speaking, it defines the process of solving newer challenges based on solutions that worked for similar past issues.

Feature Extraction – In machine learning, image processing and pattern recognition plays a dominant role. Feature Extraction begins from a preliminary set of measured data and ends up building derived values that intend to be non-redundant and informative – leading to improved subsequent learning and even better human interpretations.

Forward Chaining – Also known as forward reasoning, Forward Chaining is one of two main methods of reasoning while leveraging an inference engine. It is a widely popular implementation strategy best suited for business and production rule systems. Backward Chaining is the exact opposite of Forwarding Chaining.

Genetic Algorithm (GA) – Inspired by the method of natural selection, Genetic Algorithm (GA) is mainly used to devise advanced solutions to optimization and search challenges. It works by depending on bio-inspired operators like crossover, mutation and selection.

Pattern Recognition – Largely dependent on machine learning and artificial intelligence, Pattern Recognition also involves applications, such as Knowledge Discovery in Databases (KDD) and Data Mining.

Reinforcement Learning (RL) – Next to Supervised Learning and Unsupervised Learning, Reinforcement Learning is another machine learning paradigms. It’s reckoned as a subset of ML that deals with how software experts should take actions in circumstances so as to maximize notions of cumulative reward.

Looking for artificial intelligence certification in Delhi NCR? DexLab Analytics is a premier big data training institute that offers in-demand skill training courses to interested candidates. For more information, drop by our official website.

The article first appeared on— www.analyticsindiamag.com/25-ai-terminologies-jargons-you-must-assimilate-to-sound-like-a-pro

 

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A Beginner’s Guide to Learning Data Science Fundamentals

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A Beginner’s Guide to Learning Data Science Fundamentals

I’m a data scientist by profession with an actuarial background.

I graduated with a degree in Criminology; it was during university that I fell in love with the power of statistics. A typical problem would involve estimating the likelihood of a house getting burgled on a street, if there has already been a burglary on that street. For the layman, this is part of predictive policing techniques used to tackle crime. More technically, “It involves a Non-Markovian counting process called the “Hawkes Process” which models for “self-exciting” events (like crimes, future stock price movements, or even popularity of political leaders, etc.)

Being able to predict the likelihood of future events (like crimes in this case) was the main thing which drew me to Statistics. On a philosophical level, it’s really a quest for “truth of things” unfettered by the inherent cognitive biases humans are born with (there are 25 I know of).

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Arguably, Actuaries are the original Data Scientists, turning data in actionable insights since the 18th Century when Alexander Webster with Robert Wallace built a predictive model to calculate the average life expectancy of soldiers going to war using death records. And so, “Insurance” was born to provide cover to the widows and children of the deceased soldiers.

Of course, Alan Turing’s contribution cannot be ignored, which eventually afforded us with the computational power needed to carry out statistical testing on entire populations – thereby Machine Learning was born. To be fair, the history of Data Science is an entire blog of its own. More on that will come later.

The aim of this series of blogs is to initiate anyone daunted by the task of acquiring the very basics of Statistics and Mathematics used in Machine Learning. There are tonnes of online resources which will only list out the topics but will rarely explain why you need to learn them and to what extent. This series will attempt to address this problem adopting a “first principle” approach. Its best to refer back to this article a second time after gaining the very basics of each Topic discussed below:

We will be discussing:

  • Central Limit Theorem
  • Bayes Theorem
  • Probability Theory
  • Point Estimation – MLE’s
  • Confidence Intervals
  • P-values and Significance Test.

This list is by no means exhaustive of the statistical and mathematical concepts you will need in your career as a data scientist. Nevertheless, it provides a solid grounding going into more advanced topics.

Without further due, here goes:

Central Limit Theorem

Central Limit Theorem (CLT) is perhaps one of the most important results in all of Statistics. Essentially, it allows making large sample inference about the Population Mean (μ), as well as making large sample inference about population proportion (p).

So what does this really means?

Consider (X1, X2, X3……..Xn) samples, where n is a large number say, 100. Each sample will have its own respective sample Mean (x̅). This will give us “n” number of sample means. Central Limit Theorem now states:

                                                                                                &

Try to visualise the distribution “of the average of lots of averages”… Essentially, if we have a large number of averages that have been taken from a corresponding large number of samples; then Central Limit theorem allows us to find the distribution of those averages. The beauty of it is that we don’t have to know the parent distribution of the averages. They all tend to Normal… eventually!

Similarly if we were to add up independent and identically distributed (iid) samples, then their corresponding distribution will also tend to a Normal.

Very often in your work as a data scientist a lot of the unknown distributions will tend to Normal, now you can visualise how and more importantly why!

Stay tuned to DexLab Analytics for more articles discussing the topics listed above in depth. To deep dive into data science, I strongly recommend this Big Data Hadoop institute in Delhi NCR. DexLab offers big data courses developed by industry experts, helping you master in-demand skills and carve a successful career as a data scientist.

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

 

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Top 4 Python Industrial Use-Cases: Explained

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Top 4 Python Industrial Use-Cases: Explained

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Python is one of the fastest-growing and most popular coding languages in the world; a large number of developers use it on daily basis and why not, it works brilliantly for a plethora of developer job roles and data science positions – starting from scripting solution for sysadmins to supporting machine learning algorithms to fueling web development, Python can work wonders across myriad platforms!

Below, we’ve rounded up 4 amazing Python industrial use-cases; scroll ahead:

Insurance

Widely used in generating business insights; courtesy machine learning.

Case Study:

Smaller firms driven by machine learning gave stiff competition to a US multinational finance and insurance corporation. In return, the insurer formed teams and devised a new set of services and applications based on ML algorithms to enjoy a competitive edge. However, the challenge was that with so many data science tools, numerous versions of Python came into the picture and gave rise to compatibility issues. As a result, the company finalized only one version of Python, which was then used in line with machine learning algorithms and tools to derive specific results.

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Finance

Data mining helps determine cross-sell opportunities.

Case Study:

Another US MNC dealing in financial services showed interest in mining complex customer behavioral data. Using Python, the company launched a series of ML and data science initiatives to dig into its structured data that it has been gathering for years and correlated it with an army of unstructured data, gathered from social media and web to enhance cross-selling and retrieve resources.

Aerospace

Python helps in meeting system deadlines and ensured utmost confidentiality.

Case Study:

Recently, the International Space Station struck a deal with an American MNC dealing in military, defense and aerospace technology; the latter has been asked to provide a series of systems to the ISS. The critical safety systems were mostly written in languages, like Ada; they didn’t fare well in terms of scripting tasks, data science analysis or GUI creation. That’s why Python was chosen; it offered bigger contract value and minimum exposure.

Retail Banking

Enjoy flexible data manipulation and transformation – all with Python!

Case Study:

A top-notch US department store chain equipped with an in-store banking division gathered data and stored it in a warehouse. The main aim of the company was to share the information with multiple platforms to fulfill its supply chain, analytics, retail banking and reporting needs. Though the company chose Python for on-point data manipulation, each division came up with their own versions of Python, resulting in a new array of issues. In the end, the company decided to keep a standard Python; this initiative not only resulted in amplifying engineering speed but also reduced support costs.

As end notes, Python is the next go-to language and is growing each day. If you have dreams of becoming an aspiring programmer, you need to book the best Python Certification Training in Delhi. DexLab Analytics is a premier Python training institute in Delhi; besides Python, it offers in-demand skill development courses for interested candidates.

 

The blog has been sourced from— www.techrepublic.com/article/python-5-use-cases-for-programmers

 

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The Impact of Big Data on the Legal Industry

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The Impact of Big Data on the Legal Industry

The importance of big data is soaring. Each day, the profound impact of data analytics can be felt across myriad domains of digital services – courtesy an endless stream of information they generate. Yet, a handful number of people actually ponders over how big data is influencing society’s some of the most important professions, including legal. In this blog, we are going to dig into how big data is impacting the legal profession and transforming the dreary judiciary landscape across the globe.

Importance of Big Data

Information is challenging our legal frameworks. Though technology has transformed lives 360-degree, most of the country’s bigwigs and institutions are still clueless about how to harness the power of big data technology and reap significant benefits. The men in power remain baffled about the role of data. The information age is frantic and the recent court cases highlight that the Supreme Court is facing a tough time taming the big data.

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However, on a positive note, they have identified the reason of slowdown and are joining the bandwagon to upgrade their digital skills and upend tech modernization strategies. Data analytics is a growing area of relevance and it must be leveraged by the nation’s biggest legal authorities and departments. From tracking employee behaviors to scanning through case histories, big data is being employed everywhere. In fact, criminal defense lawyers are of the opinion that big data is altering their courtroom approaches, which have always dominated the trials with a set of certain evidence. Today, the pieces of evidences have become digital than judicial.

Boon for Law Enforcement Officials

The technology of big data has proved to be a welcoming-change for the army of law enforcement officials; the reason being efficiency in prosecuting a large number of criminals in a jiffy. Officials can now scan through piles and piles of data at a super-fast pace and handpick scam artists, hackers and delinquents. Besides law enforcers, police officers are also identifying threats and rounding up criminals before they even plan to get way.

Moreover, the prosecutors are leveraging droves of data to summon up evidence to support their legal arguments in court. That’s helping them win cases! For example, of late, federal prosecutors served a warrant to Microsoft to gain access to their data pool. It was essential for their case.

Big Data Transforming Legal Research

Biggest of all, big data is transforming the intricacies of the legal profession by altering the ways how scholars research and analyze the court proceedings. For example, big data is used to study the Supreme Court’s arguments and we have discovered that arguments are becoming more and more peculiar in their own ways.

Such research tactics will largely lead the show as big data technology tends to become cheaper and more widely popular across the market. In the near future, big data is going to be applied in a plethora of industry verticals and we are quite excited to witness impactful results.

As a matter of fact, you don’t have to wait long to see how big data changes the legal landscape. In this flourishing age of round-the-clock information exchange, the change will take no time.

Now, if you are interested in Big Data Hadoop certification in Delhi, we’ve good news rolling your way. DexLab Analytics provides state-of-the-art big data courses – crafted by industry experts. For more, reach us at <www.dexlabanalytics.com>

 
The blog has been sourced from —  e27.co/how-big-data-is-impacting-the-legal-world-20190408
 

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Now Machine Learning Can Predict Premature Death, Says Research

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Now Machine Learning Can Predict Premature Death, Says Research

Machine Learning yet again added another feather in its cap; a team of researchers tried and tested a suave machine learning system that can now predict early death. Yes, premature death can now be estimated, courtesy a robust technology and an outstanding panel of researchers from the University of Nottingham! At first, it may sound weird and something straight out of a science fiction novel, but fret not – machine learning has proved itself in improving the status of preventive healthcare and now it’s ready to venture into new unexplored medical territories.

Prediction at Its Best

Published in PLOS ONE in one of their special editions of Machine Learning in Health and Biomedicine, the study delves into how myriad AI and ML tools can be leveraged across diverse healthcare fields. The technology of ML is already reaping benefits in cancer detection, thanks to its sophisticated quantitative power. These new age algorithms are well-equipped to predict death risks of chronic diseases way ahead of time from a widely distributed middle-aged population.

To draw clear conclusions, the team collected data of more than half a million people falling within the age group of 40 and 69 from the UK Biobank. The data collection is from the period 2006-2010, followed up till 2016. With this data in tow, the experts analyze biometric, demographic, lifestyle and clinical factors in each individual subject. Robust machine learning models are used in the process.

Adding in, the team observed dietary consumption of vegetables, fruits and meat per day of each subject. Later, the team from Nottingham University proceeded to predict the mortality of these individuals.

“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics,” says Dr. Stephen Weng, assistant professor of Epidemiology and Data Science.  “We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.”

Accuracy and Outcome

The researchers involved in this ambitious project are excited to the bones. They are eager about the outcomes. They are in fact looking forward to a time where medical professionals would be able to distinguish potential health hazards in patients with on-point accuracy and evaluate the following steps that would lead the way towards prevention. “We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care”, shares Dr. Stephen Weng.

As closing thoughts, the research is expected to build the foundation of enhanced medicine capabilities and deliver customized healthcare facilities tailoring risk management for each individual patient. The Nottingham research draws inspiration from a similar study where machine learning techniques were used to predict cardiovascular diseases.

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In case, you are interested in Machine Learning Using Python training course, DexLab Analytics is the place to be. With a volley of in-demand skill training courses, including Python certification training and AI training, we are one of the best in town. For details, check out our official website RN.

 
The blog has been sourced from —
interestingengineering.com/machine-learning-algorithms-are-now-able-to-predict-premature-death
 


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Big Data Analytics for Event Processing

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Courtesy cloud and Internet of Things, big data is gaining prominence and recognition worldwide. Large chunks of data are being stored in robust platforms such as Hadoop. As a result, much-hyped data frameworks are clouted with ML-powered technologies to discover interesting patterns from the given datasets.

Defining Event Processing

In simple terms, event processing is a typical practice of tracking and analyzing a steady stream of data about events to derive relevant insights about the events taking place real time in the real world. However, the process is not as easy as it sounds; transforming the insights and patterns quickly into meaningful actions while hatching operational market data in real time is no mean feat. The whole process is known as ‘fast data approach’ and it works by embedding patterns, which are panned out from previous data analysis into the future transactions that take place real time.

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Employing Analytics and ML Models

In some instances, it is crucial to analyze data that is still in motion. For that, the predictions must be proactive and must be determined in real-time. Random forests, logistic regression, k-means clustering and linear regression are some of the most common machine learning techniques used for prediction needs. Below, we’ve enlisted the analytical purposes for which the organizations are levering the power of predictive analytics:

Developing the Model – The companies ask the data scientists to construct a comprehensive predictive model and in the process can use different types of ML algorithms along with different approaches to fulfill the purpose.

Validating the Model – It is important to validate a model to check if it is working in the desired manner. At times, coordinating with new data inputs can give a tough time to the data scientists. After validation, the model has to further meet the improvement standards to deploy real-time event processing.

Top 4 Frameworks for ML in Event Processing

Apache Spark

Ideal for batch and streaming data, Apache Spark is an open-source parallel processing framework. It is simple, easy to use and is ideal for machine learning as it supports cluster-computing framework.

Hadoop

If you are looking for an open-source batch processing framework then Hadoop is the best you can get. It not only supports distributed processing of large scale data sets across different clusters of computers with a single programming model but also boasts of an incredibly versatile library.

Apache Storm

Apache Storm is a cutting edge open source, big data processing framework that supports real-time as well as distributed stream processing. It makes it fairly easy to steadily process unbounded streams of data working on real-time.

IBM Infosphere Streams

IBM Infosphere Streams is a highly-functional platform that facilitates the development and execution of applications that channels information in data streams. It also boosts the process of data analysis and improves the overall speed of business decision-making and insight drawing.

If you are interested in reading more such blogs, you must follow us at DexLab Analytics. We are the most reputed big data training center in Delhi NCR. In case, if you have any query regarding big data or Machine Learning using Python, feel free to reach us anytime.

 

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Transforming Construction Industry With Big Data Analytics

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Transforming Construction Industry With Big Data Analytics

Big Data is reaping benefits in the construction industry, especially across four domains – decision-making, risk reduction, budgeting and tracking and management. Interestingly, construction projects involve a lot of data. Prior to big data, the data was mostly siloed, unstructured and gathered on paper.

However, today, the companies are better equipped to utilize the power of big data and employ it in a better way. They can now easily capture data with the help of numerous high-end devices and transform the processes. In a nutshell, the result of implementing big data analytics is positive and everybody involved is enjoying the benefits – namely improved decision-making, higher productivity, better jobsite safety and minimum risks.

Moreover, using the previous data, construction companies now can predict future outcomes and focus on projects that are expected to be successful. All this makes big data the most trending tool of the construction industry and for all the right reasons. The sole challenge is, however, how businesses adopt these robust changes.

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Reduce Costs via Optimization

To stay relevant and maintain a competitive edge, continuous optimization of numerous processes is important. Big data lends a helping hand to ensure the efficacy of such processes by keeping a track of all the processes from first to the very last step – making them quick and productive. With big data technology, companies can easily understand the areas where improvements are required and devise the best strategy.

Needless to say, the primary focus of optimization is to reduce costs and unnecessary downtime. Big Data is by far tackling this concern well.

Worker’s Productivity is Important

Generally, when we discuss productivity in the construction industry, it mostly concerns technology and machines – leaving behind a crucial factor, humans. Big data takes into account each worker’s productivity. It is no big deal to track their work progress. In fact, it will help increase their productivity and boost efficiency.

Furthermore, when a lot of data is at hand, companies can even analyze how their workers are interacting to discover ways to enhance their efficiency levels by replacing tools and technologies.

The Role of Data Sharing

The construction industry is brimming with data. There is so much data here that it needs another capable organization to handle such vast piles of information. Among other things, companies need to share information with their stakeholders. They also need to strategize this data for better accessibility.

Ultimately, the main task of these companies is to eliminate data silos if they really want to savor the potentials of this powerful technology to the fullest. Till date, they have been successful.

In a nutshell, we can say that big data is positively impacting the whole construction industry and is more likely to expand its horizons in the next few years. However, the companies need to learn how to imbibe this cutting edge technology to enjoy its enormous benefits and sail towards the tides of success – because big data is here to stay for long!

DexLab Analytics is a phenomenal Big Data Hadoop institute in Delhi NCR that is well-known for its in-demand skill training courses. If you are thinking of getting your hands on Hadoop certification in Delhi, this is the place to go. For more details, drop by our website.



The blog has been sourced from —  www.analyticsinsight.net/how-big-data-is-changing-construction-industry

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DexLab Analytics is Providing Intensive Demo Sessions in March

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DexLab Analytics is Providing Intensive Demo Sessions in March

The internet has spurred quite a revolution – in several sectors, including education. Interested candidates are at liberty today to learn a vast array of things and garner a humongous pool of knowledge. Online demo sessions further add to the effect. These demo sessions are state-of-the-art and in sync with the industry demands. They are one of the most effective methods of learning and upgrading skills, particularly for the professionals. They transform the learning process and for all the good reasons.

DexLab Analytics is a premier data science training institute that conducts demo sessions, online and offline regularly. These demo sessions are indeed helpful for the students. With an encompassing curriculum, a team of experts and a flexible timing, the realm of demo sessions has become quite interesting and information-laden.  

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Talking of online sessions, they are incredibly on-point and high on flexibility. With daring innovations in technology, no longer do you have to travel for hours to reach your tuition center. Instead, from the confines of your home sweet home, you can gain access to these intensive demo sessions and learn yourselves. Adding to that, the medium of learning is easy and user-friendly. The millennial generation is extremely tech-savvy that leaves no room for difficulties learning online.

Moreover, we boast of top-of-the-line faculty strength, well-versed in the art and science of data science and machine learning. With years of experience and expertise, the consultants working with us are extremely professional and knowledgeable in their respected field of study. Lastly, online demo sessions are great tools for career advancement. While working, you can easily upgrade your skills in your own time – boosting career endeavors further. The flexibility of learning is the greatest advantage.

This month, DexLab Analytics is organizing the following demo sessions; kindly take a note of the date and timing:

  • Demo session on Machine Learning, Deep Learning and Python – Saturday 16th March at 2 PM by industry professionals

  • Demo session on Data Visualization and Reporting – Saturday 23rd March at 11 AM by industry professionals

  • Demo session on Credit Risk Modelling – Saturday 16th March at 2 PM by industry professionals

For more information on big data Hadoop training in Delhi, follow DexLab Analytics.

 

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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

Deep leaning technology is taking the world by storm. It is leaving no territory untouched, not even the world of dead! Yes, this robust technology has now started hunting ghosts – for real. Of late, Nature Communication even published a paper highlighting that a ghost population has even contributed to today’s genomes.

With the help of a demographic model structured on deep learning in an Approximate Bayesian Computation framework, it is now possible to delve into the evolutionary history of the Eurasian population in sync with the present-day genetic evidence. Since it is believed that all modern humans have originated Out of Africa, the evolutionary history of the Eurasian population has been identified by introgressions from currently extinct hominins. What’s more, talking about the unknown population, the researchers believe they either trace their roots to Neanderthal-Denisova clade or simply forked early from the Denisova lineage.

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If you want to take a look at the original paper, click here — www.nature.com/articles/s41467-018-08089-7

In addition, the study reflects how the fabulous technology of AI can be leveraged in paleontology. Whether it’s about discovering unpredictable ghosts or unraveling the fading footprints of the whole evolutionary journey, deep learning and AI are taking the bull (paleontology, in this respect) by its horns. According to the paper, researchers studied deep about the evolutionary process of Eurasian population, including past introgression events in OOA (Out of Africa) populations suiting the contemporary genetic evidence and they have produced several simulated evolutionary data, like the total size of ancestral human populations, the exact number of populations, the appropriate time when they branched out from one another, the rate at which they intermixed and so on. Besides, a wide number of simulated genomes for current-day humans have been launched.

The latest and very efficient deep learning method highlights the crucial importance of genomes – they can easily let you know which evolutionary models are most likely to reveal respective genetic patterns. Moreover, if you study closely, you will find that the culture of the entire industry has changed over the past few years. Advanced computers and technology modifications have achieved ‘things’ that were simply impossible with pen and paper a few years back. Perhaps, what’s more interesting is that our perspective of seeing data has changed completely. The potent advances in AI and machine learning have demystified the ways in which algorithms work leading to more concrete shreds of evidence and end-results, which were previously not possible with the age-old traditional methods.

The blog first appeared on— www.analyticsindiamag.com/deep-learning-uncovers-ghosts-in-modern-dna

Are you interested in artificial intelligence certification in Delhi NCR? DexLab Analytics is your go-to institute, which is specialized in imparting in-demand skill training courses. Be it artificial intelligence course, data science certification or Python Spark training, DexLab Analytics excels in all – for more information, contact us today.

 

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