Best Real-Life Machine Learning Examples

Machine learning is the most important buzzword in the world of technology, alongside artificial intelligence. It has attracted mainstream attention for the ability to come up with artificial intelligence systems that could learn and improve continuously. Machine learning has offered a new avenue for technological advancements alongside introducing tools that would have been considered obsolete a few years ago. 

You can find many machine learning examples in real life, including virtual assistants such as Alexa and recommendation engines of streaming services such as Netflix. As a matter of fact, machine learning has powered some of the most notable breakthrough innovations that support modern lifestyle. Let us find out more about machine learning and some of the best examples of ML applications in real life.

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Unravelling the Importance of Machine Learning 

Before you dive into the best machine learning examples, it is important to find out why machine learning is the focal point of discussions in the domain of technology. Machine learning is a subdomain of artificial intelligence and focuses on training machines with datasets and past experiences.

Machines can learn from historical data by identifying patterns and coming up with predictions. As of now, machine learning is a $21 billion industry, and by 2029, it might grow beyond $200 billion. 

Machine learning algorithms can help AI systems learn continuously from their interactions and experiences with users and datasets. ML algorithms do not need explicit programming to perform specific tasks. The algorithms can learn patterns from sample inputs to predict and perform tasks based on their insights. 

One of the major apprehensions in the minds of people seeking examples of machine learning is the fear of overwhelming technical jargon. Some people may also assume that machine learning would empower machines and futuristic robots with the resources to take over the world.

On the contrary, machine learning has proved to be a groundbreaking innovation. Organizations have successfully leveraged machine learning in some form or another to tap into impressive benefits. For example, businesses can use ML algorithms to complete business operations faster and more effectively. 

At the same time, machine learning also plays a vital role in enhancing the quality of our lives. Therefore, business leaders and the general public have become more curious about the real-life applications of machine learning.

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Top Examples of Machine Learning in the Real World  

Machine learning systems work in a way that resembles the working mechanism of neurons and neural pathways in the brain. The top machine learning examples showcase how ML algorithms can become more effective and accurate by consuming more data.

On top of it, machine learning has become an integral part of everyday lives of people in the form of different technologies. Here is an outline of the most noticeable examples of ML use cases in the real world.

• Machine Learning in Customer Service

You can find the most popular examples of machine learning in customer service. Machine learning can not only understand customer messages but also the context and their emotions. It helps ML algorithms guide customers to the right customer service agents. On top of it, voice-based queries can utilize techniques such as natural language processing and sentiment analysis to ensure effective speech recognition.

Chatbots or virtual agents handle text-based queries, mostly on e-commerce websites. These chatbots reduce waiting times for customers and ensure high availability for a large number of users simultaneously. 

One of the prominent examples of machine learning in customer service is the Watsonx Assistant system. The AI chatbot by IBM has helped a bank answer almost 96% of all customer queries with precision. On top of that, Watson Assistant could achieve the same with consistency, speed, and availability in different languages.

Businesses also leverage machine learning to monitor social media to study customer feedback and reviews. In addition, businesses can also leverage machine learning for forecasting and decreasing customer churn.

• Machine Learning and Email Filtering 

Another prominent case of machine learning being used in the real world is email filtering. Gmail utilizes machine learning algorithms to automate the process of filtering customers’ emails into different categories. At the same time, the algorithms also detect spam mail and send it directly to the spam folder. 

Email filtering is one of the most noticeable machine learning examples in real life that showcases how ML algorithms work in the shadows. Machine learning tools can move beyond the rules of the email app and facilitate the automation of email management through email classification.

Email management automation tools like Levity utilize machine learning to identify and categorize emails through text classification algorithms. As a result, users can create personalized responses according to each category, thereby saving time and improving conversion rates.

• Machine Learning and Finance 

The next important addition among the examples of ML in the real world is the financial services sector. Most of the answers to “What is the best example of machine learning?” would point to banks.

Banks and financial service institutions leverage machine learning and deep learning to identify suspicious online transactions. In addition, banks and lenders also utilize machine learning classification algorithms alongside predictive models to identify the best borrowers. 

Stock market transactions also offer one of the best examples of the use of AI and ML. Machine learning algorithms utilize stock market data for precise forecasts of trends alongside offering suggestions for buying or selling. 

Machine learning can also carry out algorithmic trading without the need for human intervention. As a matter of fact, almost 60% to 73% of trades on the stock market are algorithmic. The best thing about ML algorithms is their ability to predict patterns, lower costs, reduce risks of human error and improve accuracy. 

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• Machine Learning and App Functionalities 

You can also find the use cases of machine learning gaining dominance on different social media platforms. The best examples of machine learning on social media are Facebook and LinkedIn. Facebook leverages machine learning for the automation of friend-tagging suggestions through face detection and image recognition.

LinkedIn utilizes machine learning to filter different objects in a newsfeed and make employment recommendations. Other apps also use machine learning in different capacities, such as Spotify, which utilizes machine learning models for generating song recommendations.

Machine learning can help in strategizing, modernizing, developing, and managing existing applications. Therefore, ML can contribute to improved efficiency alongside generating more opportunities for innovation. In addition, popular apps such as Google Translate utilize machine learning in combination with natural language processing to offer additional functionalities. For example, Google Translate can translate into more than 130 languages. 

• Machine Learning in Healthcare 

Machine learning has also fuelled promising advancements in the field of healthcare. You can find popular machine learning examples in healthcare with a primary focus on pattern recognition, which is useful in radiology imaging.

AI-powered computer vision is a useful resource for the analysis of mammograms and early lung cancer screenings. Doctors are likely to miss 40% of cancer cases by looking at mammograms for breast cancer. ML algorithms can help in improving the accuracy of identifying cancers. 

Another formidable example of machine learning in healthcare refers to the evaluation of historical patient records and results for the creation of new treatment plans. Machine learning can also play a vital role in determining the impact of genes on health through enhancements in genome sequencing and gene modification. For instance, machine learning can recognize genetic markers alongside genes that would or would not respond to specific treatments. Advanced analytics with machine learning can help in coming up with data-driven recommendations for personalized treatment or medication. 

• Machine Learning for Cybersecurity 

The examples of machine learning also find solid ground in the domain of cybersecurity. For instance, machine learning and facial recognition systems have become an integral part of authentication methods used in enterprise security.

Antivirus software can also utilize machine learning algorithms for precise detection and blocking of malware. ML classification algorithms also serve as effective resources for labelling fraud events and classification of phishing attacks. Reinforcement learning utilizes machine learning for training models to effectively identify and implement necessary measures against cyber threats. 

• Machine Learning for Sales and Marketing 

You can also notice how machine learning has become an integral part of the working of sales and marketing departments. The answers for “What is the best example of machine learning?” point at the use of ML for data analytics, SEO, lead generation and online searchers. For instance, ML algorithms can help in identifying and contacting users who exit the website or leave products in the cart.

Machine learning also empowers personalized marketing efforts through identification of products and services that meet the interests of customers. Big companies such as Netflix and Amazon utilize data science and machine learning algorithms to offer recommendations based on user’s browsing history and preferences.

Final Words

The review of the top machine learning examples showcases the massive potential of machine learning in the real world. As the demand for adoption of machine learning gains momentum, it is reasonable to assume that many other industries would adopt ML.

Awareness of examples of using ML in real world can help you measure the potential of machine learning. At the same time, you can also reflect on the significance of machine learning to revolutionize technology. Learn more about the fundamentals of machine learning and how it works right now.