Machine Learning Applications across Various Industries

This blog post covers the most common and coolest machine learning applications across various business domains-

·         Machine Learning Applications in Healthcare

·         Machine Learning Applications in Finance

·         Machine Learning Applications in Retail

·         Machine Learning Applications in Travel

·         Machine Learning Applications in Media

Automatic education applications for healthcare

Doctors and doctors will soon be able to predict the accuracy of patients with serious illnesses. Health systems will learn about the data and help patients to save money without going through unnecessary tests. Radiologists will be replaced by algorithms for learning the machine.

McKinsey Institute Global estimates that demanding mechanical education techniques well decides to make up to $ 100 billion in value-based new expectations, improves health care and creates a number of new equipment for doctors, insurance and consumers. Computer and robotics cannot replace doctors or nurses; however, the use of life-saving technology (machine learning) can significantly change health care. When we talk about the machine's mechanical efficiency, more information produces effective results, and the health industry lives on the data mines.

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 Study Drug / Industry

Inventor discover new drugs is an expensive and long process, as are thousands of compounds through a series of tests, and the only one that can lead to drug use. Learning machine can thrive one or more of these steps for the long-term process.

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Individual treatment / medication

Imagine when you visit your doctor with a type of pain in your stomach. After you ask for your symptoms, the doctor will log into the computer which produces the latest diagnosis that the doctor will need to know how to treat your condition. You have an MRI and a computer helps the chemist to know the problems that may be too small for the human eye to see. Finally, the computer will check all your medical records and your family history and compare it to the latest screening to advise the treatment system specifically designed for your problem. Learning all of the machines must be a personal sign.

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Automatic financial education applications

More than 90% of the world's financiers of world-class education and development research. Using direct online financial education helps banks to provide personalized customer services at low prices, adhere to good compliance and generate high income.

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Examples of direct financial education to identify fraud

Citibank has collaborated with a local company Feedzai fraud detection, which works in real time to identify and eliminate fraud in online banking and person looga warn the customer.

 PayPal uses machine learning to combat money laundering. PayPal has several computer labs that track millions of millions of dollars of money transfers between the legal and counterfeiting transactions between buying and selling.

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Examples of direct financial education for targeted accounts receivable

Are you thinking of how banks know about the most expensive accounts? - Confidentiality is the key learning algorithm that ensures that clients are the best of those who have big balances and debt.

Wells Fargo used machine learning to identify a group of mothers make a home in Florida with large social networks of bank customers the most influential musicians in terms of references. Mechanisms of machine learning recognize the human shapes that we have not previously identified, which helped the Wells-Fargo drive to key customers.

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Applications for machine-building applications

Project education in the retail stores is more than ever. Auditors are implementing data technologies such as Hadoop and Spark to create effective data and quickly find out that it is the first. They need to analyze real-time data and provide valuable information that can be translated into concrete results, such as buying back-ups. learning machine learning algorithms and this analysis of hardware to make this an array focusing on the retail team such as Amazon, Target, Walmart and Alibaba.

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Examples of machine learning in retail for product recommendations

According to The Realities of Online Personalization Report, 42% of retailers are using customized product recommendations using machine learning technology. It is no secret that customers always look for personalized shopping experiences, and these recommendations increase conversion rates for retailers, resulting in fantastic revenue.

The moment you start searching for items on Amazon, you'll see recommendations for products that interest you such as "Customers who bought this product also purchased" and "Customers who saw this product also seen," as well as recommendations for specific products made to measure in Homepage. and through email. Amazon uses the automatic learning algorithm of Artificial Neural Networks to generate these recommendations for you.

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Direct education applications

In 2030, there will be a solution for every single purpose. While you are working and worrying to get a car, you can get a service. For travel trips, non-motor vehicles can no longer drive when you relax and watch a movie. - ALVIN CHINA, TALOOY TECHNOLOGY TECHNOLOGY

Examples of direct trips for the active price

One of the major uses of Uber in the education system comes in the form of a higher price, a model called Geosurge on Uber. If you are late to a meeting and need to make Uber in a busy place, be prepared to pay twice the standard price. In 2011, during the New Year's Eve in New York, Uber charged $ 37 to $ 135 and a trip to a mile. Uber takes advantage of the real-time prediction method based on traffic patterns, delivery and needs. Uber received a high-cost licensing license. However, the negative value of consumer prices is stronger, so Uber uses engineering education to predict where the top demands of drivers are to prepare them to meet the needs and to significantly reduce the cost.

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Automatic application requests for social networks

Learning machine provides the most effective of the many billions of looga participate in social network users. As an employment service in the news service that targeted ads, machine learning is the heart of all the social media of its own money for a user. Social networks and chat applications are so high that users can not use their phone or email to contact their name; leave comments on Facebook or Instagram waiting for an immediate response to normal channels.

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Here are some examples of learning how to use and enjoy your social media accounts without knowing that exciting features are applications for machines:

• In the past, Facebook is used to create a user that will mark its friends, but today, the technology of deploying a network of computer labs to a network of social networks refers to their respective faces. ANN algorithm appeals to human brain structure to improve face recognition.

• The LinkedIn network is known where to apply for the next job, which connects and compares skill comparing skills while looking for a new job.

These are some of the most exciting examples of engineering education that says technology learning to use a variety of business domains, but I would like to hear other applications on machine learning if you are any knowledgeable. Share the comments below.