10 Machine Learning Applications + (Real-World Examples)

Most of us interact with machine learning almost daily. From personalized recommendations on streaming platforms to financial systems that automatically flag fraudulent transactions, there are countless ways we use AI in our everyday lives. 

The applications for machine learning are growing every day. In this article, you’ll learn more about machine learning and how it is used. Throughout, you’ll explore some online, flexible courses that can help you gain the skills you need to start using machine learning yourself. 

What is machine learning? 

Machine learning is a subfield of artificial intelligence (AI) that uses models created from algorithms trained on data sets to perform relatively complex tasks that traditionally could only be performed by humans, such as making predictions or categorizing information. As a result, machine learning is one of the most ubiquitous forms of AI used today and accounts for many of the recent advances in the goods and services that people use every day. 

Machine learning has impacted nearly every industry, and its adoption is expected to grow exponentially in the coming years. According to research published on Statista, the global market size for artificial intelligence is projected to reach nearly 826 billion US dollars by 2030, or more than four times its market size in 2024 [1]. 

The growing impact of AI and machine learning means that professionals capable of effectively working with them are often in high demand. This includes jobs like data scientists, machine learning engineers, AI engineers, and data engineers. 

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

10 real-world applications of machine learning 

Machine learning is everywhere. Yet, while you likely interact with it practically every day, you may not be aware of it. To help you get a better idea of how it’s used, here are 10 real-world applications of machine learning. 

1. Image recognition 

One of the most common uses of machine learning is image recognition. To do this, data professionals train machine learning algorithms on data sets to produce models capable of recognizing and categorizing certain images. These models are used for a wide range of purposes, including identifying specific plants, landmarks, and even individuals from photographs. 

Some common applications that use machine learning for image recognition purposes include Instagram, Facebook, and TikTok. 

2. Translation

Translation is a natural fit for machine learning. The large amount of written material available in digital formats effectively amounts to a massive data set that can be used to create machine learning models capable of translating texts from one language to another. Known as machine translation, AI professionals create models capable of translation in many ways, including through the use of rule-based, statistical, and syntax-based models, neural networks, and hybrid approaches. 

Some popular examples of machine translation include Google Translate, Amazon Translate, and Microsoft Translator. 

3. Fraud detection 

Financial institutions process millions of transactions daily. Perhaps unsurprisingly, it can be difficult for them to know which are legitimate and which are fraudulent. 

As more and more people use online banking services and cashless payment methods, the number of fraudulent transactions has similarly risen. In fact, according to a 2023 report from TransUnion, the number of digital fraud attempts in the US rose a staggering 122 percent between 2019 and 2022 [2]. 

AI can help financial institutions detect potentially fraudulent transactions and save consumers from false charges by flagging those that seem suspicious or out of the ordinary. Mastercard, for example, uses AI to flag potential scams in real-time and even predict some before they happen to protect consumers from theft in certain situations. 

4. Chatbots 

Effective communication is key for almost all businesses operating today. Whether they’re helping customers troubleshoot problems or identifying the best products for their unique needs, many organizations rely on customer support to ensure that their clients get the help they need.

The cost of supporting a well-trained workforce of customer support specialists can make it difficult for many organizations to provide their customers with the resources they require. As a result, many customer support specialists may find their schedules inefficiently packed with customers who face a wide range of needs – from those that can be easily in a matter of minutes to those that require additional time. 

AI-powered chatbots can provide organizations with the additional support they need by assisting customers with their most basic needs. Using natural language processing, these chatbots are capable of responding to consumers' unique queries and directing them to the appropriate resources so that customer support specialists can assist those with the trickiest of needs. 

Read more: What Is a Chatbot? Definition, Types, and Examples

5. Generate text, images, and videos 

Generative AI is capable of quickly producing original content, such as text, images, and video, with simple prompts. Many organizations and individuals use generative AI like ChatGPT and DALL-E for a wide range of reasons, including creating web copy, designing visuals, or even producing promotional videos. 

Yet, while generative AI can produce many impressive results, it also has the potential to produce material with false or misleading claims. If you’re using generative AI for your work, consequently, it’s advised that you provide an appropriate level of scrutiny to it before releasing it to the wider public. 

Read more: What Is ChatGPT? (and How to Use It)

6. Speech recognition 

Whether you’re driving a car, kneading dough, or going for a long run, it’s sometimes easier to operate a smart device with your voice than to stop and use your hands to input commands. Machine learning makes it possible for many smart devices to recognize speech so users can complete tasks without touching them, such as calling a friend, setting a timer, or searching for a specific show on a streaming service. 

Today, speech recognition is a relatively common feature of many widely available smart devices like Google's Nest speakers and Amazon’s Blink home security system. 

7. Self-driving cars 

Perhaps one of the more “futuristic” technological advancements in recent years has been the development of self-driving cars. While such a concept was once considered science fiction, today, there are several commercially available cars with semi-autonomous driving features, such as Tesla’s Model S and BMW’s X5. Manufacturers are hard at work to make fully autonomous cars a reality for commuters over the next decade. 

The dynamics of creating a self-driving car are complex – and indeed still being developed – but they’re primarily reliant on machine learning and computer vision to function. As the car drives from one place to another, it uses computer vision to survey its environment and machine learning algorithms to make decisions on the go. 

8. AI personal assistants

Everyone could use a bit of extra help. That’s why many smart devices come equipped with AI personal assistants to assist users with common tasks like scheduling appointments, calling a contact, or taking notes. Whether people realize it or not, whenever they use Siri, Alexa, or Google Assistant to complete these kinds of tasks, they’re taking advantage of machine learning-powered software. 

9. Recommendations 

Businesses and marketers spend a significant amount of resources trying to connect consumers with the right products at the right time. After all, if they can show customers the kinds of products or content that meet their needs at the precise moment they need them, they’re more likely to make a purchase – or simply stay on their platform. 

In the past, sales representatives at brick-and-mortar stores would match consumers with the kinds of products they’d be interested in. However, as online and digital shopping become the norm, organizations need to provide the same level of guidance for Internet users. 

To do it, modern online retailers and streaming platforms use recommendation engines that produce personalized results for consumers based on information like their geographic location and previous purchases. Some common platforms that use machine learning-based recommendation engines include Amazon, Netflix, and Instagram. 

10. Detect medical conditions 

The health care industry is awash in big data. From electronic health records to diagnostic images, health facilities are repositories of valuable medical data that can be used to train machine learning algorithms in order to diagnose medical conditions. In fact, while some researchers are already using machine learning to identify cancerous growths in medical scans, others are using it to create software that can help health care professionals make more accurate diagnoses.  

Read more: Digital Health Explained: Why It Matters and What to Know

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Explore machine learning on Coursera

As machine learning becomes more widely adopted, so too do its applications. Learn more about machine learning and its many uses with an AI-focused course or specialization on Coursera. 

In DeepLearning.AI’s Generative AI for Everyone course, you’ll learn how to use generative AI tools, how they’re made, and how they can help you increase your productivity. 

In Stanford and DeepLearning.AI’s Machine Learning Specialization, meanwhile, you’ll learn how to build machine learning models capable of both prediction and binary classification tasks. Master fundamental AI concepts and develop practical machine learning skills in as little as two months in this three-course program from AI visionary Andrew Ng.