Machine Learning (ML) has become a popular term across the global business community. Clearing the landscape for previously imagined future learning through Artificial Intelligence and robotics, machine learning has become the torchbearer of tech advancements. Machine learning algorithms and applications can now be seen across various industrial verticals, paving the way for innovation that people could only dream of a few years back. In addition, it powers and sustains regular modern lifestyles by employing smart prediction engines and algorithms. Online live TV streaming, image recognition, speech recognition, predictive analytics, etc., are some of the most popular machine learning examples.
But, before we delve into the machine learning applications and examples, let us first have a brief look at what the future of machine learning exactly is.
Machine learning is a broad term encompassing all the tools and techniques that induce new learnings and adaptiveness in computers. Machine learning applications support artificial intelligence without explicitly being programmed to perform the preferred activity. There are some sample inputs fed as a standard pattern through which the machine learning algorithm predicts and conducts tasks as per the learned pattern instead of a predefined program instruction. Examples of machine learning have served to be saviors in multiple cases where the application of strict algorithms was challenging. It learns the new process through previous patterns and completes the tasks.
One popular example of machine learning applications is dealing with our spam emails. Spam filters employ an algorithm that recognizes and dumps the received junk emails into the spam folder. Several machine learning applications in businesses such as e-commerce use algorithms and other IT security tools to contain fraud and enhance their predictive engine performance. With this said, let us now explore how these machine-learning applications in different business domains and industries are taking innovation by storm!
Examples of machine learning applications for social media can be explicitly seen on Facebook. Usually, most social media platforms use ML algorithms and methods to produce appealing and effective features. Facebook, for example, keeps a keen eye on all your activities, including likes, comments, chats, and the span you spend on what type of posts. As a result, the algorithm adapts your activities and suggests friends and pages that match your interests.
Product recommendation is one of the most famous examples of machine learning applications. Being one of the most prominent features of almost every e-commerce or online store today, it is also among the most advanced machine learning application techniques. With the help of artificial intelligence and machine learning, websites get empowered to track user behavior according to their previous purchases, search history, and cart history to offer them product recommendations.
It is an approach for detecting and cataloging a component or object in the digital image. It is one of the most notable examples of machine learning applications and AI techniques. The technique is mainly based on the intensity of pixels. In addition, the approach is further employed for improved analysis such as pattern recognition, face recognition, or face detection.
This is yet another machine learning application example. As it can translate voice into text, many software utilizes this technique to convert live voice or recorded speech into a text file. Most pertinent examples of machine learning speech recognition include voice search, speech dialing, appliance control, etc. Devices such as Google Home and Amazon Alexa are top examples of voice recognition by AI and ML.
If you were looking for machine learning application examples with mind-blowing results, then this is the one. Sentiment analysis is a real-time ML application that determines the sentiment or emotion of the user. It includes written or speech input and takes action accordingly. For example, you have written a review for a restaurant; the predictive sentiment analysis can determine the tone and emotion of the text you have written. Companies or businesses can use it for reviewing and other decision-making features.
One prominent example of this machine learning application is Grammarly. The Grammarly Chrome extension stays active when you type an email or message indicating the tone, letting you know whether it is okay, good, or bad.
Employee access control is one important example of machine learning applications in the business domain. Organizations are actively enforcing machine learning algorithms to determine the right level of access employees require in different dimensions, depending on their job profile. This application of machine learning is catching pace with the growth of the corporate culture.
With several examples of machine learning applications in healthcare introduced frequently, the demand for machine learning algorithms in this sector has significantly increased. These algorithms are capable of predicting the waiting times for patients sitting in the waiting rooms in different departments of a hospital. This model uses important factors that help determine the algorithm, staff information at various times of day, patient records, full logs of department chats, and emergency room layouts.
Machine learning algorithms are also extremely useful for detecting disease, planning therapy, and predicting the disease condition. Another vital application of machine learning in healthcare is the identification patterns of a patient’s cardiovascular history. The algorithm is developed to scan a doctor’s free-form e-notes and determine patterns of cardiovascular health. As a result, computers can now help doctors to analyze a heart patient’s current condition and arrive at a sound diagnosis instead of going through multiple previous health records.
Machine learning applications in finance are also getting famous these days. Banks use machine learning algorithms to prevent fraud and secure customer accounts from hackers. The algorithm deduces the critical factors required to consider to create a protective layer. For example, when you’re redirected to an unauthorized website, it will be filtered out and prohibited from performing any transactions.
One of the most common examples of machine learning applications is language translation. Technology does a terrific job of translating one language into another. Website development companies use this application to help users effortlessly translate languages and gain a contextual meaning of texts written in other languages. The name of this approach is “machine translation.” It enables you to interact with people living on the other end of the world. Moreover, when someone travels in a foreign land, this technology eliminates the language barrier for them and eases their communication with people speaking another language.
Another example of machine learning applications is extraction. The technique can extract structured data from unstructured information. For example, companies collect huge volumes of data through customer surveys. The algorithm automates the procedure of gathering datasets for predictive analytics tools. The real-world examples of machine learning data extraction include generating models to decode speech problems, devising strategies to contain, diagnose, and treat the disorders, and supporting treatment and treating problems quickly. Therefore, machine learning tracks and extracts information to amass huge amounts of data samples to ease the tedious process.
These examples of machine learning applications are evolving rapidly, making us thrilled about the future prospects of their course. However, while employing AI and machine learning applications, companies should ensure to get professionals with hands-on experience on board. AI and machine learning may take your business to the next level with a better experience for your customers.
September 22, 2022
December 15, 2021