Top 15 Real World Examples of Machine Learning
Machine learning is a recent breakthrough that has improved a lot of commercial and technical operations, as well as our everyday routines. It is a subset of artificial intelligence (AI) that focuses on employing statistical approaches to create intelligent systems that can learn from existing data. It is now applied in a variety of sectors and businesses. Medical diagnosis, image processing, prediction, classification, learning association, regression, and other applications are examples of machine learning.
Machine learning is applicable in a wide range of sectors and businesses, and it has the potential to expand throughout time. Here are real-world examples of machine learning at action.
Examples of Machine Learning
1. Image Recognition
We know image recognition is everywhere. From Face-ID on phones, to criminal databases, image recognition has applications. There are several instances in which an item might be classified as a digital picture. In the case of a black and white image, for example, one of the metrics is the intensity of each pixel. Each pixel in a colorful picture delivers three measures of intensity in three separate hues.
Machine learning may also recognize faces in images. In a database containing numerous persons, each person has their own category. Machine learning also applies in character recognition, which can distinguish between handwritten and printed letters. A piece of text is divisible into smaller pictures, each having a single character.
2. Speech Recognition
One term for the conversion of utterances into writing is speech recognition. It is often referred to as automated voice recognition or computer speech recognition. In this case, a software application may detect the words spoken in an audio clip or file and convert the audio to a text file. Here, a range of numbers reflect the voice input. We may additionally segment the voice stream using its intensities in other time-frequency bands.
Speech recognition is applied in a variety of areas, including voice user interfaces, voice searches, and others. Voice calling, call routing, and appliance control are all examples of voice user interfaces. It may also help with simple data entering and structuring papers.
3. Medical diagnosis
Machine learning helps in approaches and systems that aid in illness diagnosis. It can analyze clinical data and their combinations for diagnosis, for example, illness advancement prediction, therapy planning, and monitoring patients. These are notable applications of machine learning algorithms. It has the potential to aid in the incorporation of software technologies in the healthcare industry.
4. Virtual Personal Assistants
Some well-known examples of virtual personal assistants are Siri, Alexa, and Google Now. When asked over the phone, they aid in discovering information, as the name implies. All you have to do is activate them and start asking questions. To respond, your personal assistant searches for information, recalls your relevant inquiries, or sends a command to other resources (such as phone applications) to gather data. You can even delegate certain duties to your helpers.
Machine learning is a crucial component of these personal assistants since it collects and refines information depending on your prior interactions with them. This set of data then provides results that are personalized to your tastes.
5. Social Media
Social media companies use machine learning for user gain, from tailoring your news feed to better targeted advertising. Here are a few instances of things you may be discovering, utilizing, and enjoying on your social media accounts without realizing they are ML applications.
People You May Know: Machine learning is a basic idea: knowledge via experience. Facebook constantly recognises the people you connect with, the accounts you visit frequently, your hobbies, job, or a community you share with someone, and so on. On the basis of continual learning, a list of Facebook members with whom you can become friends is offered.
Face Recognition: When you post a picture with a friend, Facebook is able to identify the account of your friend automatically. Facebook analyzes the stances and projections in the image, looks for distinguishing qualities, and then matches them with persons on your friend list. The whole complex procedure takes care of the accuracy factor, yet the front end appears to be a simple application of ML.
6. Malware Filtering
Each day, around 325,000 malwares are found, and almost every malware program is 90-98 percent similar to its earlier versions. Machine learning cybersecurity tools recognise the code patterns. As a result, they easily and quickly identify fresh malware with 2-10% alteration and provide security from it.
7. Search Engine refinement
Search engines such as Google use ML to enhance their search results for you. Every time you do a search, the systems on the server monitor how you react to the results. If you open the top results and stay on the page for an extended period of time, the search engine concludes that the results it presented were relevant to the query. Similarly, if you reach the second or third page of search results but do not open any of them, the search engine assumes that the results supplied did not meet your criteria. As a result, the algorithms in the backend enhance the search results.
8. Product Recommendation
You may have noticed that the shopping website or app suggests certain things that are similar to your preferences. This certainly improves the customer experience, but did you realize that machine learning is doing the work for you? They make product suggestions taking into account your actions on the website/app, previous purchases, products liked or put in the basket, brand preferences, and so on.
9. Dangerous jobs
Among the most dangerous occupations in the world is being on the bomb squad. This is yet another case where the use of machine learning is critical in order to protect human life. Robotic systems and unmanned drones are now replacing people who do this hazardous work. Drones currently require human input, but as ML advances, these same drones will be unmanned and completely controlled by AI.
10. Email Spam Filtering
Email providers have an arsenal of tools to screen spam and safeguard privacy and user experience. Machine learning ensures that these filters are constantly updated. Spam filtering approaches enabled by ML include Multi-Layer Perceptron and C 4.5 Decision Tree Induction.
11. Transportation
Planes today employ FMS (Flight Management System), a mix of GPS, motion detectors, and computer networks, to monitor their location while in flight. However, when we extend the same approach to automobiles, the dynamics shift dramatically. There are numerous automobiles on the street, barriers to dodge, and restrictions imposed by road rules. Nonetheless, self-driving cars are becoming a possibility. The introduction of Google Maps, which pulls location coordinates from drivers’ phones, has already fixed the navigation difficulties.
12. Environment Protection
The Green Horizon Project at IBM analyzes climate information from many sensors and inputs to provide reliable, updated weather and pollution forecasts. It assists municipal officials in understanding the environmental effect of their plans. Incredible environmentally friendly solutions, ranging from self-adjusting thermostats to decentralized power grids, are appearing in the industry on a daily basis.
Big data allows machines to access and store massive volumes of data, and AI might aid in the discovery of trends and use the knowledge to create approaches to formerly unsolvable issues.
13. ElderCare
Many older people find their regular tasks to be difficult. Many people rely on outside aid to care for their aging relatives. Elder care is becoming a significant worry for people all over the world. AI-powered in-home robots are the answer. These robots can assist the elderly with daily duties, allowing them to remain autonomous and at home, therefore enhancing their general well-being.
Researchers in medicine and artificial intelligence have even tested solutions that rely on infrared cameras that can detect falls, track food and alcohol intake, anxiousness, infections, urinary frequency, comfort, water intake, sleep, mobility, and more.
14. Home Security
Artificial intelligence-powered sensors and monitors are now at the heart of new-age home security. These security devices create a database of your home’s regular visitors using face detection technology and machine learning algorithms. This enables the system to detect unwanted visitors.
Other fascinating capabilities include monitoring when you last walked your dog and receiving notifications when your children return home from school. Some of the most recent systems may instantly contact emergency services, making it a viable option to subscription-based services in the same category.
15. Fraud Detection
Machine learning has shown its ability to make cyberspace safer, with one example being the detection of financial crime online. Paypal, for example, uses ML to combat money laundering. The organization employs a combination of techniques that allows them to analyze money transfers and discern between genuine and illicit exchanges between market participants.
Summary
Machine learning is a wonderful artificial intelligence technique. Machine learning has already transformed our everyday lives and the economy in its early applications. Keep in mind that in order to truly apply the examples presented here in the real world, you will need to delve deep into the world of machine learning.