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AI joins the fight against Cancer

AI joins the fight against Cancer

Cancer is the emperor of all maladies. Finding a cure to it is one of the biggest challenges in the world of medicine. More and more men and women, one in five men and one in six women worldwide likely to be afflicted, are falling prey to the malady. It is something that has spurred on the fight against the disease even more intensely. AI and machine learning has increased the scope of groundbreaking research in the field and it is worth knowing a little about.

One reason why AI, which has made inroads into numerous sectors of the economy, has made immense advancements in the field of medical oncology is the vast amount of data generated during cancer treatment. With the assistance of AI, say scientists, this vast trove of data can be mined and worked to improve methods of diagnosis and preventive cures and treatments.

Detection of Cancer

Machine learning can lead to early detection and timely treatment in many cases. Because cancer is treated in stages, unlike other diseases, machine learning can come in handy when it comes to detection of precancerous lesions in tissues.

AI utilizing tools can assist radiologists in graphically and visually studying images by revealing suspicious lesions. This process not only reduces the work load of radiologists but it also makes possible the detection of miniscule lesions which could otherwise be overlooked.

Detection of Breast Cancer

“DeepMind and Google Health collaborated to develop a new AI system that helps in detecting breast cancer accurately at a nascent stage. Being the most common cancer in women, breast cancer, has seen an alarming rise over the past few years. Though early detection can improve a patient’s prognosis significantly, mammography, which is the best screening test currently available, is not entirely error-proof”, says a report.

To correct this, researchers at DeepMind and Google Health designed an algorithm on mammogram images and noticed AI systems reduced the recurrence of errors. They discovered that AI systems functioned better than human radiologists. A few startups in India are also laboring in the arena of cancer detection.

Predicting Cancer Evolution

Besides detection, AI is useful in the treatment of cancer as well. It is critical to the survival of patients in that it is used to predict growth and evolution of cancers which could help doctors prepare a treatment plan and save lives.

Identifying Effective Treatments

AI can play a significant role in the overall treatment of the patient, especially precision medicine which is the administering of personalized medicine from a pool of generic medication beneficial to the patient. AI can also be used to design new drugs.

Thus, AI has created a huge potential for changing the mode of treatment of cancer patients. According to the report, Exscientia is the first company, globally, to have overtaken conventional drug designing processes by automating the whole process using AI. Another company is trying to do the same in Bangalore.

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It is no surprise then that AI is being even more widely adopted across sectors of healthcare and medicine. More and more professionals, the world over, are enrolling in courses teaching AI, deep learning and machine learning. For the best such institute in India, or for the best artificial intelligence training institute in Gurgaon, do not forget to visit the DexLab website today.

 

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How AI and Deep Learning Helps In Weather Forecasting

 

How AI and Deep Learning Helps In Weather Forecasting

The world’s fight against extreme weather conditions and climate change is at the forefront of all discussions and debates on the environment. In fact, climate change is the biggest concern we are faced with today, and studying the climate has increasingly become the primary preoccupation of scientists and researchers. They have received a shot in the arm with the increase in the scope of artificial intelligence and deep learning in predicting weather patterns.

Take for instance the super cyclone Amphan that has ravaged West Bengal and Orissa. Had it not been for weather forecasting techniques, meteorologists would never had predicted the severity of the cyclone and the precautionary evacuation of thousands of people from coastal areas would not have been taken, leading to massive loss of lives. This is where the importance of weather forecasting lies.

Digitizing the prediction model

Traditionally, weather forecasting depends on a combination of observations of the current state of the weather and data sets from previous observations. Meteorologists prepare weather forecasts collecting a wealth of data and running it through prediction models. These sets of data come from hundreds of observations like temperature, wind speed, and precipitation produced by weather stations and satellites across the globe. Due to the digitization of these weather models, accuracy has improved much more than it was a few decades ago. And with the recent introduction of machine learning, forecasting has become an even more accurate and exact science.

Machine Learning

Machine learning can be utilized to make comparisons between historical weather forecasts and observations in real time. Also, machine learning can be used to make models account for inaccuracies in predictions, like overestimated rainfall.

At weather forecast institutions, prediction models use gradient boosting that is a machine learning technique for building predictive models. This is used to correct any errors that come into play with traditional weather forecasting.

Deep Learning

Machine Learning and Deep Learning are increasingly being used for nowcasting, a model of forecasting in the real time, traditionally within a two-hour time span. It provides precipitation forecasts by the minute. With deep learning, a meteorologist can anywhere in the vicinity of a weather satellite (which runs on deep learning technology) use nowcasting rather than just those who live near radar stations (which are used in traditional forecasting).

Extreme Weather Events

Deep learning is being used not only for predicting usual weather patterns, it is being used to predict extreme weather conditions as well. Rice University engineers have designed a deep learning computer system that has trained itself to predict, in accurate terms, extreme weather conditions like heat waves or cold waves. The computer system can do so up to five days in advance. And the most fascinating part is it uses the least information about current weather conditions to make predictions.

This system could effectively guide NWP (numerical weather prediction) that currently does not have the ability to predict extreme weather conditions like heat waves. And it could be a super cheap way to do so as well.

According to sciencedaily.com, with further development, the system could serve as an early warning system for weather forecasters, and as a tool for learning more about the atmospheric conditions that lead to extreme weather, said Rice’s Pedram Hassanzadeh, co-author of a study about the system published online in the American Geophysical Union’s Journal of Advances in Modeling Earth Systems.

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Thus, it is no surprise then that machine learning and deep learning is being widely adopted the world over. In India, is it being taken up as a form of study and training in metropolitans like Delhi and Gurgaon. For the best Machine Learning course in Delhi and deep learning course in delhi, check out the DexLab Analytics website today.

 

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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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Artificial Intelligence and IT Operations: A new algorithm

Artificial Intelligence and IT Operations: A new algorithm

Artificial intelligence used to automate IT operations has begun being widely termed as AIOps, a new algorithm of deep learning put to use in the field of information technology to speed up businesses and response timings to incidents occurred. It is the new rage after AI itself. And, justifiably so.

Information technology is constantly in flux, changing every minute. To keep up with it, old systems will not work. What is needed for its management is smart and fast computer programs which can keep learning and re-use learnt skills with more and more operations carried out. Trends show that worldwide spending on AI systems will hit the $77.6 billion mark in 2020, three times the amount forecasted for 2018, the IDC revealed recently.

Trends show AIOps will take centre stage when it comes to problem solving and accelerating detection of incidents and remediation.  As AIOps tools mature, IT systems will be able to work on and process a larger variety of data types in a faster and better manner, enhancing performance for more specific jobs assigned to it.

AI experts in the field say AIOps will be used to enhance and increase natural language processing, analysis of the root cause of problems, detection of anomalies, and correlation and analysis of events, among other IT functions, thus giving IT operations professionals greater control over their systems.

AI technology can help improve efficiency in vital industries like healthcare and agriculture. A case in point is the development of the Chatbot which has come to contextualize and give more intuitive and human like responses to customers.

In 2020, it is expected of IT firms to introduce data-source-agnostic solutions. This new tool will be a big boost for the industry as the more varied and variegated the data fed into an AIOps platform, the greater the insights and value the algorithms can come up with. This will directly translate to mean users can determine, more accurately, issues, foresee impacts and fathom how change can affect business-critical activities.

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One drawback of the current AIOps systems are that they take a lot of time on-boarding and its takes time training company professionals in the use of the AI software as well as feeding the software with vast amounts of data and information. This is a challenge that will have to be met in the coming few years as more and more of the IT world is adopting AI in its systems.

The AIOps is being used increasingly in Indian IT firms as well, they recognizing the need to embrace the AI juggernaut the world has bowed down to. For artificial intelligence certification in Delhi NCR one can sign up for a course at DexLab Analytics which might have the perfect machine Learning course in India for you.

 

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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.

Conclusion

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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Artificial Intelligence Jobs: Data Science and Beyond!

Artificial Intelligence Jobs: Data Science and Beyond!

Artificial Intelligence is the latest technology that the industry of computer science has been working on for quite some time now. Though it has not yet been possible to materialize the high-end AIs, weak/narrow Artificial Intelligence which includes, Siri, Cortana, Bixby, Tesla, are the ones that have grown to be simply inseparable in our daily lives. This is simultaneous with the widespread of the Artificial intelligence Course in Delhiwhich is encouraging more and more students to explore new-age technologies. 

With the extensive research and tests carried out on all these new technologies to implement them in the modern industries; AI is yielding more jobs than ever before.

Jobs Springing from the Artificial Intelligence

Artificial intelligence and data always go hand in hand because it is the data that helps us gain insight into the results. Thus, it is not surprising that the professionals utter AI and data at the same instant.

When Amazon mentioned of up-skilling 100,000 employees from the United States to make them ready for the technology of the age, they also claimed that the machines with the ability to deal with data are responsible for most of these jobs.

There have been huge changes in the figures since then, with the data mapping scientists increased to 832%, the total data scientists jumped by 505%, and the total business analysts hiked about 160%. Besides, there is also a marked demand for the other employees, who are from a non-technological background. However, most of these are associated with Artificial Intelligence, like logistics coordinator and executive; process improvement manager; transportation specialist and so on.

Thus, in contradiction to our surmises that AI and its likes will throttle our jobs and crumble every other our opportunities of the same are turning out to be false for good!

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Drawing to a Close

Whether it is Machine Learning, Data Science or Artificial Intelligence, we are noticing a rapid progress and can easily count on a better future rich with technology. However, with the increasing hardware, software and advanced computing, the need to grasp the pacing technology thoroughly is becoming predominant. Thus, Machine Learning Using PythonNeural Network Machine Learning Python and Data Science Courses in Gurgaon are rising in demand to meet the need of the mass. However, you should always go for the best Artificial Intelligence Training Institute in Gurgaon to imbibe a wholesome knowledge of the subject.

 


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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! 

 


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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.

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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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