Use cases of machine learning

Machine learning is a broad discipline with several model types, subgroups, and applications. Let’s dive deeper into this article to understand what is machine learning and its use cases. 

What is Machine Learning?

Machine learning is a subset of artificial intelligence that entails the study and use of algorithms and statistical models to enable computer systems to execute certain tasks without the need for human intervention. Instead of manual human training, machine learning models depend on patterns and inference. The machine learning algorithms detect patterns using SQL queries. There are multiple SQL certification courses available online that can help you understand the pattern detection process.

Machine learning can finish almost any task that can be completed using a data-defined pattern or set of rules. It enables businesses to automate operations that formerly required humans to do, such as answering customer service calls, accounting, and screening resumes.

A model must be taught to react to particular data in certain ways to extract machine center value, which necessitates a large amount of clean training data. The model will be able to do the task on actual data if it has successfully worked through the training data and understands the subtleties of the patterns it is learning.

Furthermore, let’s explore the use cases of Machine Learning. 

Machine Learning Use Cases

1. Customer Service Automation

Managing an ever-increasing number of online client contacts has strained many businesses. They simply don’t have enough customer service personnel on hand to handle the volume of calls they’re getting, and the traditional approach of outsourcing concerns to a contact center is just unacceptable to many of today’s customers. 

2. Cybersecurity

Cybersecurity specialists have worked hard to adapt to the ever-increasing spectrum of security threats as networks become more complicated. It’s challenging enough to keep up with constantly evolving malware and hacking tactics, but the growth of the Internet of Things (IoT) devices has completely changed the cybersecurity environment. Attacks can occur at any moment, in any location, and in any manner so implementing machine learning can help in detecting any security breaches.

3. Object Recognition

While the technology to gather and interpret data has been around for a long time, teaching computers to understand what they’re looking at has proven to be a deceptively difficult task. More and more gadgets now have object recognition capabilities thanks to machine learning algorithms. 

4. Fraud Detection

The growing quantity of online financial transactions has increased consumer awareness of numerous types of fraud. While people appreciate the ease of making purchases and payments online, they also want to know that their financial information is secure. Machine learning algorithms that can evaluate enormous volumes of transactional data to identify suspicious behavior have been used by credit card firms and banks as a response. Hence, using machine learning algorithms can help with fraud detection.

5. Communication

In every type of communication, but especially in today’s industries, avoiding mistakes and misconceptions is critical. Simple grammatical mistakes, incorrect tone, or erroneous translations can cause several difficulties in email contact, customer evaluations, video conferencing, or text-based documents in all its forms. Machine learning systems have advanced communication much beyond Microsoft’s Clippy’s heyday that can help avoid any kind of miscommunication. 

Conclusion

The breadth of applications and use cases for machine learning will undoubtedly increase in the 2020s as technology advances. With the next decade just getting started, it’s essential keeping a watch on how machine learning use cases will be used to boost productivity, save costs, and improve user experiences.