The demand for automation is developing at a rapid rate in today’s world. People, companies, and machines produce a humongous quantity of data these days. This emphasizes the need for computer systems to become increasingly sophisticated and capable of performing complex tasks.
Machine learning has a lot of use cases in various fields. In this article, we will discuss some important and really useful applications of machine learning that are often overlooked but are of great significance in various places. First, let us understand what machine learning is.
What is Machine Learning?
Machine learning is a subfield of artificial intelligence that focuses on how to turn data collected based on observations or experience into useable models using various computational techniques. A machine learning model develops and extends itself by acquiring new knowledge rather than being explicitly programmed.
Machine learning allows prediction models to be built from huge amounts of heterogeneous data from many sources. Computer systems use this data for the process of machine learning to execute tasks like grouping, computations, and pattern recognition.
What Can You Do With Machine Learning?
Now let’s see some of the important real-world use cases of machine learning.
1. Image Recognition
One of the most common uses of machine learning is image recognition. It helps to identify things like people, places, and other objects. Automatic people tagging suggestions in social media is a common example of facial recognition and image recognition.
Facebook offers an auto friend tagging suggestion option. When we submit a photo of our Facebook friends, we get an automatic tagging recommendation. Face recognition and identification algorithms powered by machine learning are used here to provide recommendations based on their names.
Facebook implemented this feature on their app based on the “Deep Facial” Facebook project, which is in charge of face recognition and individual identification in photos.
Let’s see how image recognition works. Before using an artificial neural network to recognize the image, we need to process the image and make it cleaner.
The image is broken into sections that are connected in some way. This helps to isolate sections that belong to a specific object, its boundaries, form, etc., and reduce the amount of unneeded data.
The next stage is the analysis of image features. It reveals and describes the properties of the object that a human may not notice with their eyes.
After that, the next stage is the classification of images. After all the preliminary actions, image recognition happens using artificial neural networks and deep learning methods.
Image recognition can be applied in various real-world scenarios. Self-driving cars, automatic detection of diseases, face recognition, classification and grouping of images, etc., are some examples of image recognition.
2. Speech Recognition
Speech recognition is the process of recognizing and translating spoken language into text. Speech recognition uses human inputs to allow machines to respond to a text, voice, or any other input.
Users can talk to their devices and have their sounds, and it gets transformed into text instructions that the algorithm can then process. Many smart devices such as Google Assistant, Siri, Cortana, and Alexa use speech recognition to understand voice commands and respond accordingly.
Speech recognition training enables AI models to comprehend the unique inputs found in audio. The technology behind speech recognition using machine learning has come a long way. The accuracy achieved by speech recognition algorithms these days is remarkable.
Most speech recognition software are designed to completely cover all nuances in human speech, such as speech length, voice pattern, tone frequency, and so on. However, to correctly train a voice recognition system, you must give high-quality data for processing the available input.
We all see the real-time application of speech recognition in voice assistants like Alexa, Google Assistant, Siri, etc. One major advantage of this feature is that people with impairments benefit greatly from these types of systems. If a person is unable to use his/her hands or is visually challenged, voice recognition devices can be immensely helpful.
3. Product Recommendation
On eCommerce websites, the task of recommending products to a consumer based on his/her purchasing history is referred to as product recommendation. A product recommender system is a machine learning model that recommends products, content, or services to a specific user.
A recommendation engine is a type of machine learning model that is used to rank or rate products and users. A recommender system, in its broadest meaning, is a system that predicts how a user would rate a certain item. After then, the predictions will be ranked and returned to the user.
There are two types of recommendation systems:
Filters based on content: Rather than using users’ choices, these filters employ information or features connected to the products themselves. For example, to propose movies to viewers, use features such as genre, star cast, year of release, duration, and so on.
Filters that work together: These filters, unlike content-based ones, take into account the preferences and feedback of users. Collaborative filtering is a technique for recommending movies to a viewer based on previous ratings given by different viewers to various films.
4. Traffic Prediction
When we use Google Maps, it offers us the best path with the shortest route and anticipates traffic conditions. It uses various methods to anticipate traffic conditions, such as whether traffic is clear, sluggish moving, or extremely congested.
Real-time car location is provided by Google Maps and sensors. At the same time, the average time from prior days has been taken. It collects data from the user and sends it back to its database to improve performance.
Forecasting drivable speed on specific road segments, as well as the incidence and evolution of traffic jams, is part of traffic prediction.
Process of Traffic Prediction:
Data mapping: You’ll need a detailed map with road networks and other properties to get started.
Traffic information: Then, you’ll need to gather both historical and current traffic data, such as the number of vehicles going at a specific place, their speed, and the type of vehicle they are (trucks, light vehicles, etc.). The devices that were used to collect this information are as follows: sensor technologies include loop detectors, cameras, weigh-in-motion sensors, and radars.
Information on the weather: Weather data (historical, current, and forecast) is also required because weather influences road conditions and driving speed.
Additional information about the state of the roads: There are a variety of external data sources that can provide useful traffic information. Consider posts on social media about local sporting events, local news about civic protests, or even police scanners about crime scenes, accidents, or roadblocks.
5. Spam Mail Detection
Spam detection is an issue that requires supervised machine learning. You’ll need to provide your machine learning model with a set of spam and ham (not spam) messages as samples and allow it to uncover the relevant patterns that differentiate between the two groups.
Things get a little more tricky when it comes to spam detection. The exact thing we are seeking is whether an email is “spam” or “not spam” (also known as “ham”).
In spam detection, the characteristics are the words or word combinations that appear in the body of the email. The machine learning model will try to figure out how to determine the likelihood that an email message is a spam based on its content.
When we receive a new email, it is immediately categorized as important, routine, or spam. Machine learning is the technology that allows us to receive important messages with the important symbol in our inbox and spam emails in our spam folder.
In the instance of spam detection, a trained machine learning model must be able to tell if the sequence of words in an email is more similar to spam emails or not.
Gmail employs the following spam filters:
Filter for headers
Filtering with general blacklists
Filters based on rules
Filters for permissions
Machine learning algorithms such as multi-layer perceptron and decision tree are used for email spam filtering and malware identification.
Machine learning can be of great use in our day-to-day life. Needless to say, works are being carried out to improve the quality of the above-mentioned applications. There are plenty of other real-world use cases for machine learning apart from the above-mentioned ones. If you know any other applications of machine learning, comment down below.
It will be interesting to watch the upcoming developments of machine learning applications. I hope you found this article informative and helpful. Happy coding!