10 Real-Life Applications of Machine Learning

Machine learning is a rapidly growing field of artificial intelligence that has been transforming industries across the globe. From healthcare to finance, machine learning has proven to be a game-changer, helping businesses to automate processes, improve efficiency, and make better decisions. But what exactly is machine learning, and how is it being used in the real world?

In this blog post, we will explore some of the most exciting and innovative applications of machine learning in various industries, showcasing how this technology is revolutionizing the way we work and live.

Whether you are a tech enthusiast, a business leader, or simply curious about the possibilities of machine learning, this post will provide you with a comprehensive overview of the real-life applications of this exciting field.

Related: Machine Learning for Beginners: A Comprehensive Guide

What is Machine Learning?

The word “machine learning” refers to a variety of methods and resources that enable computers to learn and adjust on their own. AI can learn with the aid of machine learning algorithms without having to be expressly programmed to do so.

The machine learning algorithm predicts and executes tasks purely based on the learned pattern and not a predefined program instruction by learning a pattern from sample inputs.

In several situations where it is impossible to implement strict algorithms, machine learning comes to the rescue. It will apply the information it has learned by studying previous patterns to the new process.

Related: The Role of Machine Learning in Predictive Analysis

1. Traffic Prediction

When predicting traffic, Google Maps is incredibly precise. If you have Google Maps open and the services turned on on your Android or iPhone, your device or the app anonymously transmits Google real-time data.

The number of cars on the route and their speed are then calculated by Google using this information or data. It is a good idea to have a backup plan in case something goes wrong.

Google has also incorporated the traffic data from an app named ‘Waze.’ The company acquired it for $1 billion in 2013. The app keeps track of traffic reports provided by the local transit authority.

Google even maintains track of the traffic patterns on a particular road to forecast the flow of traffic at a given time and place. The app will recommend a quicker route if there is more traffic so you can get to your location on time.

2. Product Recommendations

Amazon, Netflix, and other e-commerce and entertainment businesses frequently use machine learning to suggest products to users. Because of machine learning, whenever we look for a product on Amazon, we begin to see advertisements for that same product while using the same browser to browse the internet.

Google uses a variety of machine learning algorithms to comprehend user interests and makes product recommendations based on those interests.

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

3. Virtual Personal Assistants

Some of the well-known instances of virtual personal assistants include Siri, Alexa, and Google Now. When asked over the phone, they assist in locating information, as the name implies.

Simply activate them and ask queries like, “What is my schedule for today?,” “What are the flights from Germany to London,” or others of a like nature. Your assistant searches for the information, remember you’re pertinent questions, or issues a request to other sources (like phone applications) to gather the information.

You can even direct assistants for certain tasks like “Set an alarm for 6 AM next morning”, and “Remind me to visit Visa Office the day after tomorrow”.

These personal assistants use machine learning extensively because it allows them to gather and improve information based on their prior interactions with them. This collection of information is later used to produce results that are customized to your tastes.

4. Google Translate

We can immediately translate texts, phrases, and websites using Google Translate. These translations are all the result of statistical machine translation performed by machines. Based on the identified text patterns, algorithms produce these translations.

When teaching someone a new language, we typically begin by going over the vocabulary and grammatical principles before moving on to the topic of building sentences. However, because every law has an exception, learning a new language can be very challenging.

Currently, Google Translate adopts a significantly different strategy. Instead of teaching every rule of a language to the computer, what happens is that it allows the computer to discover the rules by itself. This is accomplished by Google Translate with the aid of machine learning.

Text is gathered by Google Translate from various sources. The machine scans the text after gathering the data or text to look for trends. Once the machine recognizes the pattern, it uses it repeatedly to translate identical text.

5. Online Customer Support

Today, many websites give visitors the choice of chatting with a customer service agent as they browse the site. But not every website has a real representative available to respond to your inquiries.

You converse with a robot the majority of the time. The information that these bots tend to extract from the website and show to the customers. Robots are developing over time. Due to its machine learning algorithms, they tend to better comprehend user queries and provide them with better answers.

6. Online Fraud Detection

By identifying fraudulent transactions, machine learning makes our internet transactions safe and secure. Every time we conduct an online transaction, there may be several ways for a fraudulent transaction to occur. Things like the use of fictitious accounts and identification documents and the theft of money amid a transaction can occur.

To identify this, Feed Forward Neural Network assists us by determining whether the transaction is legitimate or fraudulent.

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

7. Social Media Services

Social media platforms are utilizing machine learning for their own and user benefits, from customizing your news stream to better ad targeting. Here are a few examples of features you must be using, observing, and enjoying on social media without realizing that they are nothing more than machine learning (ML) applications.

• Face Recognition: When you submit a photo of you and a friend, Facebook recognizes that friend right away. Facebook compares the poses and projections with the individuals on your friend list after identifying any distinctive features in the image. Although the front end appears to be a straightforward application of ML, the complete process at the backend is intricate and handles the precision factor.
  
  
• People You May Know: Machine learning relies on the straightforward idea of knowledge through experiences. Facebook constantly keeps track of the friends you interact with, the profiles you frequent, your interests, your place of employment, the groups you belong to, etc. A list of Facebook users you can become friends with is recommended based on ongoing learning.
  
  
• Similar Pins: The main component of Computer Vision, a method for gleaning information from pictures and movies, is machine learning. Pinterest uses computer vision to recognize the items (or pins) in the pictures and suggests related pins in line with that information.
  
  

8. Search Engine Result Refining

Machine learning is used by Google and other search engines to enhance your search results. The algorithms at the backend monitor how you react to the findings after each search you conduct. The search engine assumes that the results it displayed were relevant to the query if you open the top results and browse the website for a while.

The search engine assumes that the results it served did not meet your requirements if you reach the second or third page of search results but do not view any of them. The algorithms at the backend enhance the search results in this manner.

9. Sentiment Analysis

One of the most essential uses of machine learning is sentiment analysis. A real-time machine learning application called sentiment analysis works to ascertain the sentiment or viewpoint of the speaker or writer.

For instance, a sentiment analyst will quickly determine the true intention and tone of a review or email (or any other type of document) that has been written. This sentiment analysis tool can be used to examine decision-making apps, review-based websites, etc.

10. Regulating Healthcare Efficiency and Medical Services

A significant portion of the healthcare industry is constantly investigating using machine learning algorithms to improve management. They predict the waiting times of patients in the emergency waiting rooms across different departments of hospitals.

The models make use of key parameters that help define the algorithm, information about the staff at different times of the day, patient records, full logs of department chats, and emergency room layouts.

Additionally, illness detection, planning of therapies, and disease situation prediction all involve the use of machine learning algorithms. One of the most essential machine learning apps is this one.

Conclusion

Businesses outside of the AI sector, such as those in retail, logistics, and transportation, already benefit from machine learning’s enhanced effectiveness and untapped potential. Humans have undergone significant lifestyle changes as a result of machine learning technologies, on which we rely heavily. We all use it, whether intentionally or unintentionally

In addition to the cases mentioned above, machine learning has demonstrated its potential in several other contexts. Share your thoughts about machine learning and how it has affected your daily life in the comments section below.

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