Machine learning is a category of artificial intelligence based on algorithms created to predict patterns from data. The more data machine learning (ML) algorithms consume, the more accurate their prediction and decision-making processes become. ML technology is so intertwined with our lives that we may not even realize it’s there in the technologies we use every day. From streaming services to social media to assistive technology, here are some commonly encountered examples of machine learning. [1].
9 examples of machine learning in action
These real-world machine learning examples show how artificial intelligence (AI) is present in our daily lives.
1. Recommendation system
Recommendation engines are one of the most popular applications of machine learning, as most e-commerce websites feature product recommendations. The website uses machine learning models to track your behavior and recognize 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 and TV shows based on a user’s previous viewing history. These algorithms are trained over time to understand your preferences and accurately predict which artists and movies you will enjoy.
2. Connection with social media
Another example of a similar training algorithm is the “People You May Know” feature on social media platforms such as LinkedIn, Instagram, Facebook, and X. Based on a user’s contacts, comments, likes, or existing connections, the algorithm suggests familiar faces from real-world networks that the user might want to connect with or follow.
3. Image recognition
Image recognition is another machine learning technique that appears in everyday life. ML allows programs to identify objects and people in images based on pixel intensity. Facial recognition is a type of image recognition. This is often used for security features such as Apple’s Face ID system, which is used to unlock company devices such as iPhones. [2]. Facial recognition may also be used by law enforcement agencies. Police officers and investigators can narrow down the list of criminal suspects by filtering databases of people to identify commonalities and matching them to faces. For example, law enforcement agencies in Telangana state have developed facial recognition tools as part of Operation Smile, a regular campaign aimed at tackling child labor and locating missing children. [3].
4. Natural Language Processing (NLP)
Just as ML can recognize images, language models can also support audio signals and manipulate them into commands and text. AI-coded software applications can convert recorded or live audio into text files.
Voice-based technology can be used for medical applications, such as helping doctors extract important medical terms from conversations with patients.
What are the four types of machine learning?
Four major types of machine learning power the technologies people use every day, from image recognition to personalized recommendations. They include:
1. Supervised learning Train models for prediction and classification using labeled data.
2. Unsupervised learning Find patterns in unlabeled data without human guidance or input.
3. Semi-supervised learning Combine a small amount of labeled data with a large unlabeled set to improve accuracy.
4. Reinforcement learning Teach the model through trial and error and reward successful results.
5. Virtual personal assistant
Virtual personal assistants are devices you have in your home, such as Amazon’s Alexa, Google Home, and Apple Siri. These devices use voice recognition technology and machine learning to capture data about what the user is requesting and how often the device accurately delivers it. It detects when the user starts speaking, what they’re saying, and executes your commands. For example, when you say “Siri, what’s the weather like today?”, Siri searches the web for weather forecasts for your location and provides detailed information.
6. Stock market prediction
Predictive analytics and algorithmic trading are common machine learning applications in the finance, real estate, and product development industries. Machine learning classifies data into groups and defines them. After classification, the analyst can calculate the probability of an action. These machine learning techniques help predict how the stock market will perform based on year-by-year analysis. Analysts can use predictive analytics and machine learning models to predict stock prices in 2026 and beyond.
7. Credit card fraud detection
Predictive analytics helps determine whether a credit card transaction is fraudulent or legitimate. Fraud examiners use AI and machine learning to monitor variables related to past fraud incidents. Use these training examples to measure the likelihood that a particular event is fraudulent.
8. Traffic volume prediction
Use Google Maps to map your commute to work or new restaurants in town, and find out when you should arrive. Google uses machine learning to build models of travel times based on historical traffic data (collected from satellites). It then uses that data based on current travel and traffic levels to predict the best route according to these factors. [4].
9. Assistive technology for the elderly
Identifying obstacles and predicting potential injuries through machine learning technology can provide valuable support to individuals with limited mobility in daily activities.
How to learn machine learning: Explore your options with Coursera
Machine learning is woven into our daily lives. From suggesting music on streaming services to predicting traffic patterns, these algorithms learn from vast amounts of data to personalize our experiences and improve our world. Machine learning can be used in many real-world applications, including recommendation systems, social media features, and self-driving car technology.
Enter the field of machine learning with the Machine Learning Specialization taught by Andrew Ng, an AI visionary who has led significant research at Stanford University, Google Brain, and Baidu. By enrolling in this beginner’s program, you’ll have the opportunity to learn the basics of supervised and unsupervised learning and how to use these techniques to build real-world AI applications.
