You might not have realized that machine learning is used by modern technology to comprehend your preferences, forecast traffic patterns, and detect speech and photos. For instance, machine learning selects the optimum route based on time, traffic, and tolls when you map your commute on Google Maps.
Although at times the idea can seem abstract, machine learning is closely related to the technology that we use on a daily basis in the current world. Using examples from real-world situations, this article will outline a few key machine learning scenarios.
4 machine learning examples in the real world
These four real-life machine learning examples illustrate this artificial intelligence (AI) concept and how it affects our day-to-day life.
1. Product recommendations
Product recommendations are one of the most popular applications of machine learning, as it is featured on most e-commerce websites. Using machine learning models, company websites can track your behavior based on your browsing patterns, previous purchases, and shopping cart history. Think of the way Amazon might recommend products for you to buy again, or complementary products like bike lights, if you recently purchased a bike helmet.
Music and movies you may like: 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, these algorithms understand your preferences to recommend new artists or films you may enjoy.
Friend suggestions on social media: Another example is the “people you may know” feature on social media platforms like LinkedIn, Instagram, Facebook, and Twitter. Based on your contacts or people you’ve previously followed; the algorithm suggests familiar faces from your real-life network that you might want to connect with in the virtual world.
2. Image recognition
Image recognition is another example of machine learning that appears in our day-to-day life. With the use of machine learning, programs can identify an object or person in an image based on the intensity of the pixels.
Image recognition in health care: Image recognition is frequently used in health care to help doctors provide medical diagnoses by identifying an abnormality in an x-ray as cancerous or by identifying key markers of another disease. This eliminates the potential for human error.
Recognizing faces in law enforcement: Machine learning can also aid in facial recognition for 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 suspects.
3. Speech recognition
Just like machine learning can recognize images, it can also translate speech into text. Software applications coded with AI can convert recorded and live speech into text files. Not only that, but machine learning can also translate speech or text from one language to another.
Voice assistants: The most common examples of speech recognition are devices you might have in your own homes, such as Amazon’s Alexa, Google Home, or the Apple iPhone’s Siri. These devices detect its own name when you start speaking, 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.
Speech recognition in medical context: Speech recognition also has medical applications. For example, voice-based technologies are helping doctors interpret the conversation to separate medical terminology from the conversation. While this tool is a long way from making trustworthy clinical decisions, other types of voice assistants provide patients with reminders to “take their medication” as if they have a home health aide by their side.
4. Predictive analytics
Predictive analytics is a common type of machine learning that is applicable to industries as wide-ranging as finance, real estate, and product development. 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.
Stock market prices: Predictive analytics includes predicting how the stock market will perform based on year-to-year analysis. A stock such as Google or Apple likely has relatively steady growth, despite minor setbacks. According to predictive analytics machine learning models, analysts can predict the stock price for 2025.
Credit card fraud: 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, in order to measure the likelihood that a specific event was fraudulent activity.
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