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Popular Applications of Machine Learning
Popular Applications of Machine Learning

October 6, 2022

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Introduction 

Today's technology has made data science & machine learning a buzzword, developing exceptionally quickly. Without realizing it, we use machine learning daily in applications like Google Maps, Google Assistant, Alexa, etc. Here are the top machine learning applications happening around the world.

 

  • Image Recognition

Image recognition is one of the most widely used applications of machine learning. It is used to identify things like digital photos, people, locations, and items. Frequent use of facial recognition and image recognition is an automatic buddy suggestion.

 

Facebook offers us an automatic buddy tagging recommendation option. The face identification and recognition technique used in machine learning gives us an automated tagging recommendation with names whenever we submit a photo of one of our Facebook friends.

The "Deep Facial" technology from Facebook, which handles facial recognition and person identification in photographs, forms the foundation of this system.

  • Speech Recognition

When using Google, we have the option to "Search by voice," which falls under speech recognition and is a well-known machine learning application.

 

Converting spoken orders into writing is referred to as speech recognition, often known as "Voice to text" or "Computer speech recognition." Speech recognition applications currently employ machine learning methods extensively. Speech recognition technology is used by Alexa, Google Assistant, Siri, Cortana, and Microsoft Cortana to carry out voice commands.

  • Traffic prediction

When we wish to travel to a new location, Google Maps comes in handy since it offers us the best route and anticipates traffic conditions.

 

To predict traffic conditions, such as whether it will be apparent, moving slowly, or congested, it employs two techniques:

 

  • Real-time car position provided by sensors and the Google Maps app
  • On similar days in the past, the average time was taken.

 

Every user of Google Maps contributes to its development. It receives data from the user and returns it to its database to improve speed.

 

  • Product recommendations

Amazon, Netflix, and other e-commerce and entertainment businesses frequently utilize machine learning to propose products to users. Because of machine learning, if we look for a product on Amazon, we begin to see advertisements for the same product when using the same browser to explore the internet.

 

Using various machine learning techniques, Google determines user interests and suggests products based on those interests.

 

Similar to this, machine learning is also used to propose TV shows, movies, and other entertainment options when we use Netflix.

 

  • Self-driving cars

Self-driving automobiles are one of the most intriguing uses of machine learning. Self-driving cars heavily rely on machine learning. Tesla, the most well-known carmaker, is working on a self-driving car. In order to train the automobile models to recognize people and objects while driving, unsupervised learning was used.

 

  • Email Spam and Malware Filtering

Every new email we get is immediately classified as essential, common, or spam. Machine learning is the technology that enables us to consistently get necessary emails marked with the important sign in our inbox and spam emails in our spam box. Here are a few spam filters that Gmail employs:

 

  • Filtering Content
  • headline filter
  • filters for general blacklists
  • filters with rules
  • Allowance filters

 

For email spam filtering and virus identification, certain machine learning methods are utilized, including Multi-Layer Perceptron, Decision tree, and Naive Bayes classifier.

 

  • Virtual Personal Assistant

We have a variety of virtual personal assistants, including Siri, Cortana, Alexa, and Google Assistant. They assist us in discovering the information using our voice commands, as the name says. Our voice commands to these assistants, such as "Play music," "Call someone," "Open an email," and "Schedule an appointment," among others, may support us in a variety of ways.

 

These virtual assistants rely heavily on machine learning methods. These assistants capture our vocal commands, transmit them through a cloud server, decode them using ML algorithms, and then respond as necessary.

 

  • Online Fraud Detection

Machine learning keeps our online transactions safe and secure by recognizing fraudulent activities. Every time we conduct an online transaction, there may be a number of methods for a fraudulent transaction to occur, including the use of fictitious accounts and identification documents and the taking of money during a transaction. In order to identify this, Feed Forward Neural Network assists us by determining if the transaction is legitimate or fraudulent.

 

Each legitimate transaction has an output transformed into a set of hash values, which are then used as the following round's input. It identifies fraud and increases the security of our online transactions since there is a distinct pattern for each legitimate transaction that changes for fraudulent ones.

 

  • Stock Market trading

Trading on the stock market frequently makes use of machine learning. Since there is always a chance that share prices may go up and down, machine learning's long short-term memory neural network is utilized to anticipate stock market patterns.

 

  • Medical Diagnosis

Machine learning is utilized in medical research to diagnose disorders. As a result, medical technology is developing quickly and can now create 3D models that can pinpoint the precise location of brain lesions.

 

It makes it easier to discover brain tumors and other conditions connected to the brain.

 

  • Automatic Language Translation

These days, it is not an issue at all if we travel to a new location where we are unfamiliar with the language because machine learning also assists us in this by translating the text into our native tongues. This function is offered by Google's GNMT (Google Neural Machine Translation), which automatically uses neural machine learning to translate text into our native tongue.

 

A sequence-to-sequence learning method, combined with picture recognition and text translation from one language to another, is the technology underpinning automated translation.

 

Conclusion

 

I hope this article will help you understand how machine learning and data science are used in our daily lives. Every step we take is data for a company. From learning this, you can know the importance of machine learning and data science and the future growth of these fields. To master data science and machine learning,

 


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