9 Real-Life Machine Learning Examples

Machine learning systems mimic the structure and function of neural networks in the human brain. The more data machine learning (ML) algorithms consume, the more accurate their predictions and decision-making processes become. ML technology is so closely interwoven with our lives that you may not even notice its presence within the technologies you use daily. Read on to explore a few commonly encountered machine learning examples, from streaming services to social media to assistive technology. 

9 machine learning examples in the real world

These real-life examples of machine learning demonstrate how artificial intelligence (AI) is present in our daily lives.

1. Recommendation systems

Recommendation engines are one of the most popular applications of machine learning, as product recommendations are featured on most e-commerce websites. Using machine learning models, websites track your behaviour to recognise patterns in your browsing history, previous purchases, and shopping cart activity. This data collection is used for pattern recognition to predict user preferences.

Companies like Spotify and Netflix use similar machine-learning algorithms to recommend music or TV shows based on your previous listening and viewing history. Over time and with training, these algorithms aim to understand your preferences and accurately predict which artists or films you may enjoy.

2. Social media connections

Another example of a similar training algorithm is the “people you may know” feature on social media platforms like LinkedIn, Instagram, Facebook, and X. Based on your contacts, comments, likes, or existing connections, the algorithm suggests familiar faces from your real-life network that you might want to connect with or follow.

3. Image recognition

Image recognition is another machine learning technique that appears in day-to-day life. With the use of ML, programs can identify an object or person in an image based on the intensity of the pixels. This type of facial recognition is used for password protection methods like Apple's Face ID and law enforcement. By filtering through a database of people to identify commonalities and matching them to faces, police officers and investigators can narrow down a list of crime suspects. For example, law enforcement in Telangana has developed a facial recognition tool as part of Operation Smile, a periodic campaign aimed at tackling child labour and locating missing children.

4. Natural language processing (NLP)

Like ML can recognise images, language models can also support and manipulate speech signals into commands and text. Software applications coded with AI can convert recorded and live speech into text files.

Voice-based technologies can be used in medical applications, such as helping doctors extract important medical terminology from a conversation with a patient.

5. Virtual personal assistants

Virtual personal assistants are devices you might have in your homes, such as Amazon’s Alexa, Google Home, or Apple iPhone’s Siri. These devices use speech recognition technology and machine learning to capture data on what you're requesting and how often the device delivers accurately. They detect when you start speaking and what you’re saying and deliver on the command. For example, when you say, “Siri, what is the weather like today?” Siri searches the web for weather forecasts in your location and provides detailed information.

6. Stock market predictions

Predictive analytics and algorithmic trading are common machine learning applications in finance, real estate, and product development industries. Machine learning classifies data into groups and then defines them with rules set by data analysts. After classification, analysts can calculate the probability of an action.

These machine-learning methods help predict how the stock market will perform based on year-to-year analysis. Analysts can use predictive analytics and machine learning models to predict the stock price for 2025 and beyond.

7. Credit card fraud detection

Predictive analytics can help determine whether a credit card transaction is fraudulent or legitimate. Fraud examiners use AI and machine learning to monitor variables involved in past fraud events. They use these training examples to measure the likelihood that a specific event was fraudulent activity.

8. Traffic predictions

Using Google Maps to map your commute to work or a new restaurant in town provides an estimated arrival time. Google uses machine learning to build models of how long trips will take based on historical traffic data (gleaned from satellites). It then takes that data based on your current trip and traffic levels to predict the best route according to these factors.

9. Assistive technology for the elderly 

Identifying obstacles and predicting potential injuries through machine learning technology can offer valuable support to individuals with limited mobility in their daily activities. 

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Machine learning is woven into the fabric of our daily lives. From suggesting music on streaming services to predicting traffic patterns, these algorithms learn from vast data to personalise our experiences and improve our world. Machine learning is available in many real-world applications, including recommendation systems, social media features, and self-driving car technology.

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