9 Real Machine Learning Examples

Important points

Examples of machine learning include Netflix’s recommendation engine, image recognition features like Face ID, and Google Maps’ traffic predictions.

• Other uses for machine learning include building connections on social media, developing self-driving technology, and converting live audio into text files.

• Types of machine learning include supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, and reinforcement learning.

• Machine learning is used in technologies such as virtual personal assistants, stock market predictions, and credit card fraud detection.

Learn more about machine learning examples from streaming services to social media to self-driving cars. If you’re ready to get started in this field, enroll in Stanford’s Machine Learning Specialization and DeepLearning.AI. Learn machine learning techniques, model evaluation, model training, predictive modeling, and more in just two months.

Machine learning definitions and 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 prediction and decision-making processes become. Examples of machine learning can be seen in everyday technologies such as Netflix’s recommendation engine, image recognition features like Face ID, and Google Maps’ traffic predictions. As you learn more about how machine learning is used, you’ll gain a deeper understanding of how it’s used and how it’s already impacting your life.

9 machine learning examples

These real-world machine learning examples show how artificial intelligence (AI) is present in our daily lives.

1. Recommendation system

Recommendation engines are a common use of machine learning, as product recommendations are featured on many e-commerce websites. 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.

read more: What is pattern recognition?

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 (formerly Twitter). 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 technology that appears in our daily lives. ML allows programs to identify objects and people in images based on pixel intensity. This type of facial recognition is used in password protection methods such as Face ID and by law enforcement. 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.

4. Natural Language Processing (NLP)

Similar to how ML recognizes images, language models can also support audio signals and perform commands and operations on text. AI-coded software can convert recorded or live audio into text files.

Voice-based technology can be used in medical settings, such as helping doctors extract important medical terms from conversations with patients. Although this tool is not sophisticated enough to make reliable clinical decisions, other voice recognition services provide reminders to patients to “take their medication” as if a home health aide were nearby.

5. Virtual personal assistant

Virtual personal assistants are devices in your home, such as Amazon Alexa, Google Home, and Siri on your Apple iPhone. These devices use a combination of voice recognition technology and machine learning to obtain data about what you are requesting and how often the device accurately delivers it. Detect when the user starts speaking, what they are saying, and execute 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 techniques in industries such as finance, real estate, and product development. Machine learning classifies data into groups and uses rules set by data analysts to define the groups. 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 machine learning models to predict stock prices this year 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 or new restaurants in town, and get an estimated arrival time. Google uses machine learning to build models of travel times based on historical traffic data (collected from satellites). It then takes data based on current travel and traffic and predicts the best route according to these factors.

9. Self-driving car technology

A frequently used type of machine learning is reinforcement learning, which is used to power self-driving car technology. Self-driving car company Waymo uses machine learning sensors to collect data about the car’s surrounding environment in real time. This data helps guide the car’s response in different situations, such as a person crossing the road, a red light, or another car on the highway.

Learn more about machine learning

Subscribe to the Career Chat newsletter on LinkedIn for industry insights, skill-building tips, and networking opportunities. Next, explore free resources to learn more about machine learning.

With Coursera Plus, you can learn at your own pace and earn certifications from over 350 leading companies and universities. Get access to over 10,000 programs with a monthly or yearly subscription. Please check the course page to ensure your chosen program is included.