In 1952, the term “machine learning” was first used. Almost 70 years later, we are using machine learning on a daily basis: Face and Touch ID, Siri, Alexa, Uber, and even Computer-Aided Detection. For example, CAD software can spot 52% of breast cancer cells, a year before patients are diagnosed. What is machine learning technology, and what are its application areas? Let’s get all these straightened out.
There are two types of techniques employed by machine learning: supervised and unsupervised learning.
Supervised learning trains a model on known input and output data so that it can collect data or produce a data output from the previous experience. It helps you to solve various types of real-world computation problems. Supervised learning uses classification and regression techniques to develop predictive models.
Classification techniques classify input data into categories, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Typical applications include medical imaging, speech recognition, and credit scoring.
Regression techniques are used while working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment. These models predict continuous responses, for example, changes in temperature or fluctuations in power demand.
Unsupervised learning finds all kinds of unknown patterns in data. It helps to find features that can be useful for categorization. The most common unsupervised learning technique is clustering. Applications for cluster analysis include gene sequence analysis, market research, and object recognition.
Financial services. Financial institutions and businesses use machine learning for two purposes: to identify important insights (e.g., investment opportunities, clients with high-risk profiles) and prevent fraud.
Government. Government agencies use machine learning to identify ways to increase efficiency, save money, detect fraud, and minimize identity theft.
Healthcare. The technology helps medical experts analyze data to identify trends or red flags that may lead to improved diagnosis and treatment.
Retail. Websites recommend items you might want to buy based on previous purchases using machine learning to analyze your buying history.
Oil and gas. Machine learning helps to find new energy sources, analyze minerals in the ground, predict refinery sensor failure, streamline oil distribution to make it more cost-effective.
Transportation. Data analysis and modeling are important tools for delivery companies and public transportation to make routes more efficient and predict potential problems.
Virtual personal assistants. Some of the popular examples are Siri, Alexa, and Google Now. Machine learning is an important part of these PAs as they collect and process the information on the basis of your previous involvement with them.
Traffic predictions. GPS navigation services save our current location at a central server for managing traffic. This data is then used to build a map of the current traffic.
Online transportation. When booking a cab, the app defines price surge hours by predicting the rider demand.
Video surveillance. Such systems are powered with AI that tracks unusual behavior of people like standing motionless for a long time, stumbling, or napping on benches, etc. And when such activities are reported, machine learning collects them and uses further, helping to improve the surveillance services.
Social media. Here are a few examples of machine learning in action: face recognition and people you may know on Facebook, similar pins on Pinterest.
Online customer support. Websites use chatbots that extract information from the website and present it to the customers.
Search engine result refining. Search engines use machine learning to make your search results more accurate. For example, if you reach the second or third page of the search results and don’t open any of them, the search engine estimates that the results served did not match requirements.
Why is machine learning important? — It helps us to deal with large amounts of data and improve our experience in almost every area of life. What’s more, machine learning is the drive to labor automation, which allows businesses to profit from the more human and creative side of work.