Applications of Machine learning
Modern technology is all about machine learning, which is growing very quickly. Everyday tools like Google Maps, Google Assistant, Alexa, and others use machine learning without us even realizing it.
Below are some of the most popular real-world applications of machine learning:
- Image Recognition:
Image recognition is one of the applications of machine learning . It is used to identify items, people, locations, digital photos, and so forth. The most common application of image recognition and facial identification is automatic buddy tagging suggestion:
Facebook has a tool called auto friend tagging recommendation. When we submit a photo with our Facebook friends, we receive an automatic tagging recommendation with their names, which is powered by machine learning’s face identification and recognition algorithms.It is based on the Facebook project “Deep Face,” which handles face recognition and human identification in photos.
- Speech Recognition:
Google offers a feature called “Search by voice,” which falls under speech recognition and is a well-liked machine learning application.
Other names for speech recognition include “Speech to text” and “Computer speech recognition.” Speech recognition is the process of translating spoken instructions into written. Many voice recognition applications currently make extensive use of machine learning algorithms. Speech recognition technology is used by Alexa, Cortana, Siri, and Google Assistant to obey voice commands.
- Traffic prediction:
When we wish to travel to a new location, we use Google Maps, which anticipates traffic conditions and provides us the best route with the least travel time.
With the aid of two methods, it forecasts traffic conditions, including whether it is clear, moving slowly, or extremely congested:
- Real-time car position data from sensors and the Google Maps app
- At the same time, average time has been spent on previous days.
Every user of Google Maps contributes to the improvement of this program. To enhance performance, it retrieves data from the user and delivers it back to its database.
- Product recommendations:
Many e-commerce and entertainment companies, including Amazon, Netflix, and others, employ machine learning extensively to recommend products to users. Because of machine learning, if we look for a product on Amazon, we began seeing advertisements for the same goods while using the same browser to explore the internet.
Google suggests products to users based on the things they are interested in using different machine learning algorithms.
Similarly, Netflix recommends episodes and movies to users based on their viewing habits using machine learning.
- Self-driving cars:
Among the most fascinating applications of machine learning are self-driving cars. Autonomous vehicles heavily rely on machine learning. Tesla, the most well-known automaker, is developing technologies for autonomous driving. Unsupervised learning is being used to teach the automobile models to identify people and objects while driving.
- Email Spam and Malware Filtering:
Every new email we get is immediately classified as spam, normal, and important. Machine learning is the technology that enables us to consistently get essential emails in our inbox with the important symbol and spam emails in our spam box. Gmail uses the following spam filters:
- Content Filter
- Header filter
- General blacklists filter
- Rules-based filters
- Permission filters
Email spam filtering and virus identification are two applications for machine learning techniques such the Multi-Layer Perceptron, Decision Tree, and Naïve Bayes classifier.
- Virtual Personal Assistant:
For example, Google Assistant, Alexa, Siri, and Cortana are all virtual personal helpers that we use. As the name implies, they assist us in finding the information by using our voice instructions. With merely vocal commands, these assistants can aid us in a number of ways, including playing music, making phone calls, opening emails, making appointments, and more.
These assistants record our vocal commands, transmit them to a cloud server, decode them using machine learning algorithms, and then respond appropriately.
- Online Fraud Detection:
By finding fake transactions, machine learning makes our internet shopping safer and more secure. A illegal online transaction could happen in a number of ways, such as through fake accounts or IDs, or by someone stealing our money in the middle of the transaction. We use the Feed Forward Neural Network to check if the transaction is real or fake in order to find this.
Each valid transaction’s output is transformed into some hash values, which are then used as input in the next round. Each genuine transaction has a distinct pattern that changes for the fraudulent transaction, so it detects it and makes our online transactions more safe.
- Stock Market trading:
Machine learning is commonly applied in stock market trading. There is always the possibility of share price fluctuations in the stock market, thus machine learning’s long short term memory neural network is utilized to predict stock market movements.
- Medical Diagnosis:
Another Applications of Machine Learning is Medical Diagnosis.Machine learning is utilized in medical science to diagnose illnesses. Because of this, medical technology is developing quickly, and 3D models that can precisely predict the location of brain lesions may now be produced.It greatly facilitates the detection of brain tumors and other illnesses pertaining to the brain.