Use cases of Machine Learning in our lives

Machine learning has already spread into many aspects of everyday life that it can be useful for us to remember many cases of its impact on certain industries. It is becoming popular, and some companies are using it in a variety of ways, including developing cybersecurity, fraud protection, and optimizing self-driving cars.

Empowering business growth through innovative technologies such as IoT, predictive analytics, and artificial intelligence has become a norm in Information Technologies, and machine learning is in the lead, as software apps are getting smarter to improve personal lives. By virtue of significant improvements in hardware and Big Data, machines can sense, learn, interact, predict, and respond to solve industry difficulties.

There are a plenty of use cases for machine learning in our lives. Here’s a look at some of them for this technology.

Data Security

Malware is an enormous and increasing problem. In 2014, Kaspersky Lab had identified 325,000 new malware every day. Under this condition, humans and even powerful security solutions can’t cope, which is why machine learning and deep learning are needed. As mentioned before Eli David, CTO of institutional intelligence company Deep Instinct: “Nearly all new malware differs less than 2% from previous malware.” Their learning model has no problem with the 2–10% varieties and can predict which files are malware with high accuracy. Machine learning algorithms can scan for exemplars in how data in the cloud is accessed, and report irregularities that could predict security gaps.

Financial Trading

For obvious reasons, many people are trying to predict how the stock markets will change on any given day. Many famous trading firms use established methods to predict and perform trades at high speeds and high volume. Humans can’t race with machines when it comes to using immense amounts of data or the rate with which they can perform a trade.

Fraud Detection

Machine learning is getting better at detecting possible chances of fraud over many different fields. For instance, PayPal is using machine learning against money laundering. The company has engines that analyze millions of transactions and can accurately identify between legal and fraudulent transactions between customers and sellers.

Recommendations

If you use services such as Netflix or Amazon, you probably know about this case. Machine

learning algorithms match your activity to the millions of other users to determine what you might like to watch next or get from shops. These recommendations can recognize that there might be other family members whose favorite TV shows may vary, and it is getting smarter all the time.

Online Search

Perhaps, online search is the most popular use of machine learning today. Google and other search giants like that are regularly developing what the search engine understands. The program observes how you react to the results every time you execute a search. If you click the first page and stay there, we can think that you got the information you wanted to find.

On the other hand, if you started to type in a search string other words, we can guess that machine can’t cope with a task. The program can learn from that kind of mistakes to provide more accurate result in the future.

Machine learning also can be related to the healthcare. But the role of machine learning in the entire healthcare is controversial. However, we can use machine learning in personal healthcare. People have to be conscious about their health. Through machine learning, people can prevent some of the diseases and change their lifestyles. They will use the power of machine learning to make it more personal, sophisticated and comprehensive, and Vinnd is actually about this.