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How AI Is Facilitating Digital Marketing

How AI Is Facilitating Digital Marketing

Artificial Intelligence has transformed the world of digital marketing by making it ultra intelligent and intuitive. Almost every platform used by the digital marketer is powered by some form of AI or an AI-powered machine learning model.

If we were to define what AI marketing is, according to a report by Forbes, it is a method of leveraging technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns.

How AI Works In Marketing Strategies

AI plays a very important role in eliminating guesswork when it comes to customer interactions online like in email marketing. Big Data Analytics, machine learning and other related processes gain insights into target audience behaviour. “With these insights, you can create more effective customer touch points.”

Moreover, it is gradually automating processes that were once dependent on human beings. Content generation, PPC ads, and even web design and video marketing are all possible applications for AI marketing.

Marketing Campaigns

AI, in the world of digital marketing, can streamline and optimize marketing campaigns. “It can also eliminate the risk of human error”. It acts as a support system to shore up efforts born out of human ingenuity with its data driven reports and analyses.

While AI might be able to launch a marketing campaign all on its own, human attributes like empathy, compassion and the art of storytelling are still needed to shape up the soul of an online marketing campaign.

Content Generation And Curation

“At present, content marketing has ballooned into a global industry. It’s so prevalent that some refer to it as the only type of marketing.” Moreover, AI powered content marketing strategies are also becoming a rage.

AI can be used potentially for both curating and generating content. Already, companies are using AI for automated content generation at a basic level. But in the long run, “AI could generate viable topics for writers, or even develop initial drafts of content based on certain parameters”.

Digital Advertising

AI is also gradually transforming the way businesses advertise. In fact, today’s digital advertising strategies all have a basic level of AI powered models processing them.

AI works with the help of algorithms in its systems. “These systems operate autonomously, placing the right kinds of ads in front of the right kinds of people based on complex algorithms and big data.” This feature service is known as “programmatic advertising.”


Chatbots have become the latest game changer when it comes to the marketing industry. They are the first interface customers encounter on many websites today, giving the website a human touch, excelling at answering customers’ frequently asked questions.

“The key fascination with chatbots is the impact they can have on the customer experience. For some businesses, there aren’t enough employees or hours in the day to answer customer queries quickly. Chatbots allow customers to help themselves.”

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Behavior Analysis And Predictive Analytics

More and more companies are beginning to hire data scientists and programmers for their marketing departments. There “are so many data sets (on the Internet) that humans alone can’t possibly hope to analyze them all.”

“Using machine learning and big data analysis, AI is able to provide businesses with deep insights into their customer(s’ behaviour). Not only will businesses be able to hyper-personalize interactions, but…they’ll also be able to predict future customer behaviours based on the data collected.”

For more information on AI powered systems, do peruse the DexLab Analytics website today. DexLab Analytics is a premiere institute that offers artificial intelligence certification in Delhi NCR.



The AI Revolution in The Education Sector

The AI Revolution in The Education Sector

Artificial Intelligence (AI) is revolutionizing innumerable aspects of our lives, education being one of them. AI has transformed the way we learn, the relationship between the student and the teacher and the very manner in which our curriculum is perceived. This article, the third part of a series on the applications of artificial intelligence, delineates how AI has come to transform the education sector, as we know it.

The biggest contribution of AI to the education sector has been towards enhancing and streamlining the system of teaching students with varying needs across the spectrum, from elementary schools to adult learning centers. Students can be mentally developed in the left side of the brain with more analytical skills or they can be mentally developed in the right side of the brain with more creative and literary skills. Likewise, there may be students with different interests and passions. A strictly uniform curriculum does not suit all students of the same class because people differ in their learning ability and interests.

AI-Enabled Hyper-Personalization

AI is thus being used to customise curricula according to specific needs of each student of a single class. This is being done through the power of machine learning via a method called hyper-personalization. The AI powered system studies and examines the profile of a student and prescribes suitable curricula for her/him. According to a report, it is expected that by the year 2024 onwards, almost 50 percent of learning management tools will be powered by AI capabilities. These AI-enabled e-Learning tools will touch over $6 Billion in market size by 2024.

Smart Learning Tools

Machine Learning and AI are also defining the way hyperper sonalized and on-demand digital content is created to digitise the learning environment. Now students do not have to rote-learn chapter after chapter from textbooks. They are absorbing learning material in the form of condensed bits of information in the form of smaller study guides, chapter summaries, flashcards, as well as short smart notes designed for better reading and comprehension. Learning is therefore becoming gradually paperless. AI systems also have an online interactive interface that helps in putting in place a system of feedback from students to professors regarding areas they are facing trouble understanding.

Digital Conversations

AI systems are also being used to develop the system of tutoring with personalized conversational education assistants. These autonomous conversational agents are capable of answering questions, providing assistance with learning or assignments, and strengthening concepts by throwing up additional information and learning material to reinforce the curriculum. “These intelligent assistants are also enhancing adaptive learning features so that each of the students can learn at their own pace or time frames”. 

Adoption of Voice Assistants 

In addition, educators are relying heavily on using voice assistants in the classroom environment. Voice assistants such as Amazon Alexa, Google Home, Apple Siri, and Microsoft Cortana have transformed the way students interact with their study material. In the higher education environment, universities and colleges are distributing voice assistants to students in place of traditionally printed handbooks or hard-to-navigate websites.

Assisting Educators

AI powered systems are not only helping students with course work, they are also empowering teachers with teaching material and new innovative ways to educationally express themselves. It is easier to explain a theory with the help of picture cues and graphical representation than mere definitions. The Internet has become a treasure trove of teaching material for teachers to borrow from. Also, teachers are burdened with responsibilities “such as essay evaluation, grading of exams…ordering and managing classroom materials, booking and managing field trips, responding to parents, assisting with conversation and second-language related issues…Educators often spend up to 50% of their time on non-teaching tasks.”AI powered systems can help streamline these tasks and handle repetitive and routine work, digitise interaction with parents and guardians and leave educators with more time to teach students.

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When it comes to higher learning, in India at least, more and more artificial intelligence and machine learning institutes are opening up. DexLab Analytics is a premiere artificial intelligence course in Delhi that trains professionals in both AI and machine learning.


How AI and Machine Learning are Helping Fight Coronavirus

How AI and Machine Learning are Helping Fight Coronavirus

A Toronto based AI-startup detected the outbreak of coronavirus, a large family of viruses which infect the respiratory tract of human beings and animals, hours after the first few cases were diagnosed in Wuhan in December 2019.

More than 100,000 people the world over have been infected by the novel coronavirus since then and more than 4000 people have died, most in China.

The start-up team confirmed their findings and informed their clients about an “unusual pneumonia” in a market place in Wuhan a week before Chinese authorities and international health bodies made formal announcements about the virus and the epidemic. The key to the company’s ability to detect and warn of a possible outbreak of an epidemic is AI and big data.

NLP and Machine Learning

The company uses natural language processing or NLP and machine learning to, says a report, “cull data from hundreds of thousands of sources, including statements from official public health organizations, digital media, global airline ticketing data, livestock health reports and population demographics. It’s able to rapidly process tons of information every 15 minutes, 24 hours a day.”

This information becomes the basis of reports compiled by computer programmers and physicians. Also, they do not just detect the outbreak of a disease but also track its spread and the consequences.

In the case of COVID-19, the company besides sending out an alert, correctly identified the cities that were highly connected to Wuhan using data on global airline ticketing “to help anticipate where the infected might be travelling.”


“Already, the COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets,” a report says.

The vast amount of X-rays and scans people across the world are undergoing in this outbreak of coronavirus has strained medical resources and systems across the world. That is why AI and machine learning models are being trained to read accurately vast amounts of data tirelessly, and efficiently.

Thermal Scanners

China has already deployed AI-powered thermal scanners at railway stations in major cities to read and record, from a distance through infrared, body temperatures of persons passing to detect a fever. This technology has to a large extant reduced stress on institutions across the country.

But it must be noted that AI is set to become a huge firewall against infectious diseases and pandemics not only by powering diagnostic techniques but by identifying potential vaccines and lines of treatment against the next coronavirus and COVID-19 itself within days.

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Also, AI and big data are helping revolutionize the medical management system in China. With the outbreak of the pandemic, China hospitals are using robots to reduce the stresses piled on medical staff. Ambulances in the city of Hangzhou are assisted by AI in navigation to help them reach patients and people suspecting an infection faster.

“Robots have even been dispatched to a public plaza in Guangzhou in order to warn passersby who aren’t wearing face-masks…China is also allegedly using drones to ensure residents are staying at home and reducing the risk of the coronavirus spreading further.”

In India, though the virus has been detected in some states, it has not spread as alarmingly as it has in other countries. It is now more than ever important to concentrate on building more robust and competent Artificial Intelligence courses in Delhi and Machine Learning courses in India.


Applications of Artificial Intelligence: Agriculture

Applications of Artificial Intelligence: Agriculture

This article, the first part of a series, is on the application of artificial intelligence in agriculture. Popular applications of AI in agriculture can be sectioned off into three aspects – AI powered robots, computer vision and seasonal forecasting.


Firstly, companies are now gradually adopting AI powered machines to automate agricultural tasks such as harvesting larger volumes of crops faster than human workers. For instance, companies are using robots to remove weeds and unwanted plants from fields.

Computer Vision

Secondly, companies are using computer vision and deep learning algorithms to process and study crop and soil health. For instance, farmers are using unmanned drones to survey their lands in real time to identify problem areas and areas of potential improvement. Farms can be monitored frequently using these machines than they can be with farmers doing so on foot.

Seasonal Forecasting

Thirdly, AI is used to track and predict environmental impacts such as weather changes. “Seasonal forecasting is particularly valuable for small farms in developing countries as their data and knowledge can be limited. Keeping these small farms operational and growing bountiful yields is important as these small farms produce 70% of the world’s crops,” says a report .

The India story

In India, for instance, farmers are gradually working with technology to predict weather patterns and crop yield. Since 2016, Microsoft and a non-profit have together developed an AI sowing application which is used to guide farmers on when to sow seeds based on a study of weather patterns, local crop yield and rainfall.

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In the year 2017, the pilot project was broadened to encompass over 3,000 farmers in Andhra Pradesh and Karnataka and it was found that those farmers who received the AI-sowing app advisory text messages benefitted wherein they reported 10–30% higher yields per hectare.


Moreover, farmers across the world have begun to turn to chatbots for assistance and help, getting answers to a variety of questions and queries regarding specific farm problems.

Precision Farming

Research predicts the precision agriculture market to touch $12.9 billion by 2027. Precision agriculture or farming, also called site-specific crop management or satellite farming, is a concept of farm management that utilizes information technology to ensure optimum health and productivity of crops.

With this increase in the volume of satellite farming, there is bound to be an increase in the demand for sophisticated data-analysis solutions. One such solution has been developed by the University of Illinois. The system developed aims to “efficiently and accurately process precision agricultural data.”

A professor of the University says, “We developed methodology using deep learning to generate yield predictions…”


The application of artificial intelligence to analyze data from precision agriculture is a nascent development, but it is a growing one. Environment vagaries and factors like food security concerns have forced the agricultural industry to search for innovative solutions to protect and improve crop yield. Consequently, AI is steadily emerging as the game changer in the industry’s technological evolution.

It is no surprise then that AI training institutes are mushrooming all across the world, especially in India. For the best artificial intelligence certification in Delhi NCR, do check out the DexLab Analytics site today.


Budget 2020 Focuses on Artificial Intelligence in a Bid to Build Digital India

Budget 2020 Focuses on Artificial Intelligence in a Bid to Build Digital India

The Indian technology industry has welcomed the 2020 budget for its outreach to the sector, specially the Rs 8000 crore mission for the next five years on Quantum Computing. The budget has been praised in general for its noteworthy allocation of funds for farm, infrastructure and healthcare to revive growth across sectors in the country.

According to an Economic Times report, Debjani Ghosh, President, NASSCOM, reacting to the budget, said, “Budget 2020 and the finance minister’s speech has well-articulated India’s vision on not just being a leading provider of digital solutions, but one where technology is the bedrock of development and growth’.

Industry insiders lauded the budget for the allocation on Quantum Computing, the policy outline for the private sector to construct data center parks and the abolition of the Dividend Distribution Tax. The abolition of the Tax had been a long standing demand of the industry and the move has been welcomed. The building of data parks will help retain data within the country, industry experts said.

Moreover, while announcing the budget this year, Finance Minister Nirmala Sitharaman spelt out the government’s intentions of utilizing, more intensely, technology, specially artificial intelligence and machine learning.

These will be used for the purposes of monitoring economic data, preventing diseases and facilitating healthcare systems under Ayushman Bharat, guarding intellectual property rights, enhancing and improving agricultural systems and sea ports and delivery of government services.

Governments the world over have been emphasising the deployment of AI for digital governance and research. As per reports, the US government plans and intends to spend nearly 1 billion US dollars on AI-related research and development this year.

The Indian government has also planned to make available digital connectivity to citizens at the gram panchayat level under its ambitious Digital India drive with a focus on carrying forward the benefits and advantages of a digital revolution by utilizing technology to the fullest. One lakh gram panchayats will be covered under the Rs 6000 crore Bharat Net project wherein fibre connectivity will be made available to households.  

“While the government had previously set up a national portal for AI research and development, in the latest announcement, the government has continued to offer its support for tech advancements. We appreciate the government’s emphasis on promoting cutting-edge technologies in India,” Atul Rai, co-founder & CEO of Staqu said in a statement, according to a report by Live Mint.

The Finance Minister also put forward a plan to give a fillip to manufacturing of mobiles, semiconductor packaging and electronic equipment. She iterated that there will be a cost-benefit to electronics manufacturing in India.

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Thus, this article shows how much the government of India is concentrating on artificial intelligence and machine learning with a push towards digital governance. It shows that the government is recognising the need to capitalise on the “new oil” that is data, as the saying goes. So it is no surprise then that more and more professionals are opting for Machine Learning Course in India and artificial intelligence certification in delhi ncr. DexLab Analytics focuses on these technologies to train and skill professionals who want to increase their knowledge base in a digital first economy.


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AI – A Great Opportunity For Cyber Security Solutions

AI - A Great Opportunity For Cyber Security Solutions

AI and machine learning are the new rage in the computing world. And for reasons justified. With advancement in technology, the threat to technological systems and businesses online has also advanced and become more complex.

Cyber criminals are constantly coming up with newer mechanisms to break into cyber systems for theft or disruption. Thus, the cyber security industry is in a fix over what it can do to enhance security features of existing systems. AI and Machine Learning are the answer to its woes.

Artificial Intelligence and Machine Learning work on large sets of data, analyzing them and finding patterns in them. AI helps interpret data and make sense of it to yield solutions and ML learns up intuitively how to spot patterns in the data. The two go hand in hand and complement each other.

Cybersecurity solutions pivot on the science of finding and spotting patterns and planning the right response to these. They have the ability to tap into data and detect a set of code as malicious, even if no one has noticed it or flagged it before. Thus, it becomes complementary to AI in that it involves the cyber security software to be tutored to detect and alert the user about an anomaly or trigger an alarm if a corruption crosses the threshold without being prompted.  

Artificial Intelligence and Machine Learning are used in Spam Filter Applications, Network Intrusion Detection and Prevention, Fraud detection, Credit scoring, Botnet Detection, Secure User Authentication, Cyber security Ratings and Hacking Incident Forecasting.

They are much faster than human users deploying software to detect of fight cyber attacks and they do not tire unlike their human counterparts while assessing tons of data and malicious aspects of those data. They are thus not prone to desensitization that a human user would be prone to.

Application of AI in cyber security solutions is akin to taking things up a notch higher up. Without AI, cyber security would lose the option of having the software learn by itself by merely observing sets of data and user patterns.

An AI system would develop a digital fingerprint of the user based on his habits and preferences. This would help in the event of someone other than the user trying to break into his or her system. And AI cyber security systems do this work 24X7, unlike a human user who would spend limited time scanning for malicious codes or components.

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AI and machine learning, since their inception, have transformed the world of cyber security forever. With time, both aspects of the computing world will refine and mature. It is only a matter of time before a user’s cyber security system becomes tailored to her needs.

And it is thus not surprising that more and more professionals are opting for artificial intelligence courses to equip themselves with relevant coursework. The world is moving to reap the benefits of AI intelligence. So, if you are interested in doing the same, opt for an artificial intelligence course in delhi or a Machine Learning course in India by enrolling yourself with DexLab Analytics.


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An In-depth Analysis of Game Theory for AI

An In-depth Analysis of Game Theory for AI

Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes. With the rapid rise of AI, along with the extensive time and research we are devoting to it, Game Theory is experiencing steady growth. If you are also interested in AI and want to be well-versed with it, then, opt for the Best Artificial Intelligence Training Institute in Gurgaon now!

Games have been one of the main areas of focus in artificial intelligence research. They often have simple rules that are easy to understand and train for. It is clear when one party wins, and frankly, it is fun watching a robot beat a human at chess. This trend of AI research being directed towards games is not at all an accident. Researchers know that the underlying principles of many tasks lie in understanding and mastering game theory. Both AI and game theory seek to find out how participants will react in different situations, figuring out the best response to situations, optimizing auction prices and finding market-clearing prices.

Some Useful Terms in Game Theory

  • Game: Like games in popular understanding, it can be any setting where players take actions and its outcome will depend on them.
  • Player: A strategic decision-maker within a game.
  • Strategy: A complete plan of actions a player will take, given the set of circumstances that might arise within the game.
  • Payoff: The gain a player receives from arriving at a particular outcome of a game.
  • Equilibrium: The point in a game where both players have made their decisions and an outcome is reached.
  • Dominant Strategy: When one strategy is better than another strategy for one player, regardless of the opponent’s play, the better strategy is known as a dominant strategy.
  • Agent: Agent is equivalent to a player.
  • Reward: A payoff of a game can also be termed as a reward.
  • State: All the information necessary to describe the situation an agent is in.
  • Action: Equivalent of a move in a game.
  • Policy: Similar to a strategy. It defines the action an agent will make when in particular states
  • Environment: Everything the agent interacts with during learning.

Different Types of Games in Game Theory

In the game theory, different types of games help in the analysis of different types of problems. The different types of games are formed based on number of players involved in a game, symmetry of the game, and cooperation among players.

Cooperative and Non-Cooperative Games

Cooperative games are the ones in which the players are convinced to adopt a particular strategy through negotiations and agreements between them.

Non-Cooperative games refer to the games in which the players decide on their strategy to maximize their profit. Non-cooperative games provide accurate results. This is because in non-cooperative games, a very deep analysis of a problem takes place.

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Normal Form and Extensive Form Games

Normal form games refer to the description of the game in the form of a matrix. In other words, when the payoff and strategies of a game are represented in a tabular form, it is termed as normal form games.

Extensive form games are the ones in which the description of the game is done in the form of a decision tree. Extensive form games help in the representation of events that can occur by chance.

Simultaneous Move Games and Sequential Move Games

Simultaneous games are the ones in which the move of two players (the strategy adopted by two players) is simultaneous. In a simultaneous move, players do not know the move of other players.

Sequential games are the ones in which the players do not have a deep knowledge about the strategies of other players.

Constant Sum, Zero Sum, and Non-Zero Sum Games

Constant sum games are the ones in which the sum of outcome of all the players remains constant even if the outcomes are different. 

Zero sum games are the ones in which the gain of one player is always equal to the loss of the other player. 

Non-zero sum games can be transformed to zero sum game by adding one dummy player. The losses of the dummy player are overridden by the net earnings of players. Examples of zero sum games are chess and gambling. In these games, the gain of one player results in the loss of the other player.

Symmetric and Asymmetric Games

Symmetric games are the ones where the strategies adopted by all the players are the same. Symmetry can exist in short-term games only because in long-term games the number of options with a player increases. 

Asymmetric games are the ones where the strategies adopted by players are different. In asymmetric games, the strategy that provides benefit to one player may not be equally beneficial for the other player.

Game Theory in Artificial Intelligence

Development of the majority of the popular games which we play in this digital world is with the help of AI and game theory. Game theory is used in AI whenever there is more than one person involved in solving a logical problem. There are various algorithms of Artificial Intelligence which are used in Game Theory. Minimax algorithm in Game Theory is one of the oldest algorithms in AI and is used generally for two players. Also, game theory is not only restricted to games but also relevant to the other large applications of AI like GANs (Generative Adversarial Networks).

GANs (Generative Adversarial Networks)

GAN consists of 2 models, a discriminative model and a generative model. These models are participants on the training phase which looks like a game between them, and each model tries to better than the other.

The target of the generative model is to generate samples that are considered to be fake and are supposed to have the same distribution of the original data samples; on the other hand, the target of discriminative is to enhance itself to be able to recognize the real samples among the fake samples generated by the generative model.

It looks like a game, in which each player (model) tries to be better than the other, the generative model tries to generate samples that deceive and tricks the discriminative model, while the discriminative model tries to get better in recognizing the real data and avoid the fake samples. It is the same idea of the Minimax algorithm, in which each player targets to outclass the other and minimize the supposed loss.

This game continues until a state where each model becomes an expert on what it is doing. The generative model increases its ability to get the actual data distribution and produces data like it, and the discriminative becomes an expert in identifying the real samples, which increases the system’s classification task. In such a case, each model satisfied by its output (strategy), this is called Nash Equilibrium in Game Theory.

Nash Equilibrium

Nash equilibrium, named after Nobel winning economist, John Nash, is a solution to a game involving two or more players who want the best outcome for themselves and must take the actions of others into account. When Nash equilibrium is reached, players cannot improve their payoff by independently changing their strategy. This means that it is the best strategy assuming the other has chosen a strategy and will not change it. For example, in the Prisoner’s Dilemma game, confessing is Nash equilibrium because it is the best outcome, taking into account the likely actions of others.


So in this article, the fundamentals of Game Theory and essential topics are covered in brief. Also, this article gives an idea of the influence of game theory artefacts in the AI space and how Game Theory is being used in the field of Machine Learning and its real-world implementations.

Machine Learning is an ever-expanding application of Artificial Intelligence with numerous applications in the other existing fields. Besides, Machine Learning Using Python is also on the verge of proving itself to be a foolproof technology in the coming years. So, don’t wait and enrol in the world-class Artificial Intelligence Certification in Delhi NCR now and rest assured! 


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Machine Learning in the Healthcare Sector

Machine Learning in the Healthcare Sector

The healthcare industry is one of the most important industries when it comes to human welfare. Research analysis from the U.S. federal government actuaries say that Americans spent $3.65 trillion on health care in 2018(report from Axios) and the Indian healthcare market is expected to reach $ 372 billion by 2022. To reduce cost and to move towards a personalized healthcare system, the industry faces three major hurdles: –

1) Electronic record management
2) Data integration
3) Computer-aided diagnoses.

Machine learning in itself is a vast field with a wide array of tools, techniques, and frameworks that can be exploited and manipulated to cope with these challenges. In today’s time, Machine Learning Using Python is proving to be very helpful in streamlining the administrative processes in hospitals, map and treat life-threatening diseases and personalizing medical treatments.

This blog will focus primarily on the applications of Machine learning in the domain of healthcare.

Real-life Application of Machine learning in the Health Sector

  1. MYCIN system was incepted at Stanford University. The system was developed in order to detect specific strains of bacteria that cause infections. It proposed a good therapy in 69% of the cases which was at that time better than infectious disease experts.
  2. In the 1980s at the University of Pittsburgh, a diagnostic tool named INTERNIST-I was developed to diagnose symptoms of various diseases like flu, pneumonia, diabetes and more. One of the key functionalities of the INTERNIST-I was to be able to detect the problem areas. This is done with a view of being able to remove diagnostics’ likelihood.
  3. AI trained by researchers from Pennsylvania has been developed recently which is capable of predicting patients who are most likely to die within a year. This is assessed based on their heart test results. This AI is capable of predicting the death of patients even if the figures look quite normal to the doctors. The researchers have trained the AI with 1.77 million electrocardiograms (ECG) results. The researchers have made two versions of this Al: one with just the ECG data and the other one with ECG data along with the age and gender of the patients.
  4. P1vital’s PReDicT (Predicting Response to Depression Treatment) built on the Machine Learning algorithms aims to develop a commercially feasible way to diagnose and provide treatment of depression in clinical practice.
  5. KenSci has developed machine learning algorithms to predict illnesses and their cure to enable doctors with the ability to detect specific patterns and indicators of population health risks. This comes under the purview of model disease progression.
  6. Project Hanover developed by Microsoft is using Machine Learning-based technologies for multiple purposes, which includes the development of AI-based technology for cancer treatment and personalizing drug combination for Acute Myeloid Leukemia (AML).
  7. Preserving data in the health care industry has always been a daunting task. However, with the forward-looking steps in analytics-related technology, it has become more manageable over the years. The truth is that even now, a majority of the processes take a lot of time to complete.
  8. Machine learning can prove to be disruptive in the medical sector by automating processes relating to data collection and collation. This is highly profitable in terms of cost-effectiveness. Newer algorithms such as Vector Machines or OCR recognition are designed to automate the task of document reading and classification with high levels of precision and accuracy.

  9. PathAI’s technology uses machine learning to help pathologists make faster and more accurate diagnoses. Furthermore, it also helps in identifying patients who might benefit from a new and different type of treatments or therapies in the future.

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To Sum Up:

As the modern technologies of Machine Learning, Artificial Intelligence and Big Data Analytics are tottering forth in multiple domains, there is a long path they need to walk to ensure an unflinching success. Besides, it is also important for every one of us to be accustomed to all these new-age technologies.

With an expansion of the quality Machine Learning course in India and Neural Network Machine learning Python, all the reputed institutes are joining hands together to bring in the revolution. The initial days will be slow and hard, but it is no doubt that these cutting edge technologies will transform the medical industry along with a range of other industries, making early diagnoses possible along with a reduction of the overall cost. Besides, with the introduction of successful recommender systems and other promises of personalized healthcare, coupled with systematic management of medical records, Machine Learning will surely usher in the future for good! 



Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows

Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows

According to Forbes, Machine Learning is quickly growing up to be the biggest technology for the progress of businesses of the future. Furthermore, it will be able to add another $2.6 trillion in value, to the sales and marketing industry by 2020. Even in the field of manufacturing and logistics, it is estimated to add up to $2 trillion.

We are already seeing the extensive support that the AI-driven technology is lending to varied businesses which have joined hands with Machine Learning. This collaboration is bringing forth shocking results for the businesses, improving customer relationships, fueling sales and increasing the overall efficiency of the industry.

The total investments in Machine Learning are estimated to scale up reaching the $77 billion mark. So, if you want to enrol yourself for quality Machine Learning courses then, avail of the best Machine Learning course in India.


To Brief About Machine Learning

Machine Learning is a brand new and extremely progressive discipline at the core of which lies mathematics, statics and artificial intelligence (AI).

The basic difference between Artificial Intelligence and Machine Learning is that the former deals with the engineers writing programs for the AI to carry out specific tasks. Whereas, Machine Learning demands the engineers to write algorithms that can teach computers to write programs for themselves.

Machine Learning stresses primarily on developing the intelligence of a program and its capability of learning from past experiences. Thus, they learn from every previous interaction and each of the experiences from the past and finally, churns out the fitting solution, no matter what the circumstance is.

Therefore, a large number of businesses are incorporating Machine Learning, leading to the growth of their businesses and making their business future proof.

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To list down some of the ways how Machine Learning boosts the business performance are:

  • This new technology aids in developing software to understand the natural human language.
  • Machine Learning further improves the efficiency of logistics and transportation networks.
  • It also aids in building preventive maintenance, thereby lessening the equipment breakdowns and increasing profits.
  • Machine Learning can also be extremely useful in collecting consumer data to analyse customer profiles. This, in turn, will maximise sales and improve brand loyalty.

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