Top 4 Real-time Applications of Machine Learning in 2022

Machine learning helps systems develop algorithms that use relevant data as a source for self-learning. Machine Learning deals with the application of AI and permits systems to learn from the occurrences themselves without being explicit programming. Its capabilities have exceeded human expectations and placed them in many industries. 

So far, we've seen several predictions on the future possibilities of AI (Artificial Intelligence). Now, let's talk about how different sectors and industries use machine language in their day-to-day operations to achieve the best results. 

In this article, I will look into the best machine learning applications in various industries and the real-life application of machine learning in detail.

1. Retail business 

Machine learning has become the latest trend in retail, and retailers have started implementing big data technologies like Spark to obliterate data processing problems. 

Retailers have started executing machine learning algorithms to use this data. These algorithms will utilize the datasets to automate the analysis process and help retailers achieve desired growth. 

Let's see an example below:

Have you ever encountered similar products or products you want while using an online shopping website? You are most likely to have seen such. 

Many retail giants worldwide use machine learning algorithms to convince customers to buy their products and services. For example, Amazon utilizes neural network algorithms, and Alibaba uses an "E-Commerce Brain" to send product recommendations to customers. All these things are not magic, but they all happen through machine learning algorithms. 

2.  Travel and aviation

Machine learning has set a blazing trail in the world of transportation. Self-driving cars can control driving by allowing the driver to relax or have free time. 

A popular online transportation tycoon, Uber transportation, is a great example. Uber employs various machine learning algorithms to make the ride as convenient and comfortable as possible for the customer. Have you ever thought of these questions?

How do you agree on a fair for a ride?

How does Uber stimulate ridesharing by conforming to the destinations of different people? 

We need to understand the concept below to get the answers to all these questions.

Machine learning on the goUber built a pricing model called Surge Pricing, which was later dubbed "George" by Uber. The model can recognize traffic patterns so that a customer can charge the right price.

In 2011, Uber charged customers double the price due to heavy traffic and increased demand. Uber uses real-time prognostic modeling to assess traffic patterns to estimate supply and demand. Using the price increase model, drivers will learn the areas where demand will be high and can prepare in advance to get there, which would cause prices to dip significantly.

3.  Financial sector

Large cases of fraud are recorded every year, and billions of dollars are stolen by hackers worldwide. It has negative consequences for the country's economy and the people who lost their money. 

Have you heard of an occurrence where someone was getting calls from the bank and knew they had a suspicious transaction on their account? The machine learning algorithms are responsible for this effect. Machine learning has developed inventive solutions. 

Machine learning is prevalently recognized for fraud detection in the banking and finance sector. Machine learning can analyze a large number of data sets. It even examines large amounts of financial transactions and identifies the occurrence of unusual activity.

Let's see an example here.

PayPal (an international online financial giant) uses machine learning algorithms to monitor money laundering. It adopts several machine learning tools that compare billions of transactions and determine what is right and what is not between buyers and sellers. The algorithm processes each transaction and places a fraud detection score. Once it exceeds ordinary, the account will be put under restriction. 

4. Healthcare sector

New tides are ready to buzz in the healthcare industry. Medical systems are improving and learning from data to suggest necessary tests by eliminating long-time diagnoses by doctors. Medical specialists, nurses, doctors, and other medical personnel will accurately predict how long a patient will be alive. 

Machine learning algorithms will soon replace the radiologist's impact. Large numbers of health data are generated and stored for analysis and sampling, but humans can't perform analysis. Machine learning finds a way to derive patterns that automatically evaluate unprocessed data. Precision Medicine allows doctors to formulate a particular drug using machine language.

Conclusion:

Machine Learning is an extraordinary subfield of Artificial Intelligence (AI), and its contribution to the advancement of technology is incalculable. We have witnessed numerous breakthroughs in executing machine learning in many fields. Machine learning has frequently been spurting towards developing each area with its implications.