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What are the applications of Machine Learning?
There are various applications of machine learning which are as follows −
Social media services − Machine learning is an essential role in personalizing news feed to superior advertisement focusing over social media. Facebook needs machine learning to display news feed to the user based on its interests by treating items clicked earlier by that user.
Facebook always takes note of the friends that it can linked with, the profiles that it can visit, interests, workplace, and on the basis of this continuous learning, a file of Facebook users are suggested for us to become friends with.
The Face Recognition nature of Facebook also needs ML to tag the friends in an image. Facebook tests the poses and projections in the images, notices the specific features, and then connects them with the person in the friend's list. The whole process is implemented with the support of ML and is implemented so quickly at the backend that it tags the person as soon as it can upload its picture.
Email spam and malware filtering − Email spam and malware filters have inbuilt machine learning to recognize spam emails. On the basis of emails it is denoted as spam or not, the system understands and recognizes new mail as spam or not, automatically.
Generally, a malware’s code is 90–98% similar to its prior versions. The system security programs that includes machine learning learns the coding design and identify new malware very effectively and provide protection against them.
Search engine result refining − Google and multiple search engines need machine learning to enhance search results for us. Each time it can execute a search, the algorithms at the backend maintain a watch on how it can respond to the results. If it can open the top results and visit on the internet page for long, the search engine consider that the results it showed were in accordance with the query.
Accordingly, if it can reach the second or third page of the search results but do not open someone results, the search engine computes that the results served did not connect requirement. This is the method that machine learning trains itself at the backend to enhance search results.
Online fraud detection − Machine learning is also supporting in creating cyberspace more safe and tracking monetary frauds online. For instance, Paypal is using ML for preservation against money laundering. The company needs a group of ML tools that supports them to compare millions of transactions taking place and differences among legitimate or illegitimate transactions taking place among the buyers and sellers.
Medicine − With the advent of automation, medical data are accessible in electronic structure. The ML algorithms are providing doctors to learn diseases in a best manner by turning the medical data into medical knowledge.
Computational biology − Biologists are collecting multiple data about human DNA. The ML algorithms are supporting them to learn and identify the relationship between several genes and associated human features.
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