There are no schools for them yet, but apparently, machines can be educated. The branch of artificial intelligence (AI) called “machine learning (ML)” aims to develop computer systems that learn and improve from experience without any human intervention or programming.
During this process, machines are provided with vast amounts of data, which they analyze for patterns and then learn from using examples. Over time, the systems are able to automatically make their own decisions and adjust their actions accordingly.
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What Are the Different Types of Machine Learning?
ML can fall under two main categories—supervised and unsupervised learning.
ML in supervised learning basically teaches machines through examples. During training, the system is presented with tons of labeled data to help it master particular tasks.
Unsupervised learning, on the other hand, involves using algorithms to identify patterns in a dataset to detect similarities and group information into different categories. Unlike supervised learning, unsupervised learning can push through even without labeled data.
While these two are the known main ML categories, another is gaining popularity quickly—semi-supervised learning. This concept brings together supervised and unsupervised learning. In it, a system only needs a smaller number of labeled data compared to unlabelled data to train.
What Industries Use Machine Learning?
ML has been in existence for years, but its popularity surged recently as more and more industries recognized its value. Gleaning insights from real-time data allows organizations to work more efficiently. Some of the sectors benefiting from ML are:
Financial Services and Insurance
Organizations primarily use ML for fraud prevention. They mine for data to determine if a potential client has a high-risk profile. It can also be used in cyber surveillance for fraud investigations. ML is also useful for gaining investment insights, such as identifying the best time for trading stocks.
ML has also been revolutionary for government agencies, particularly those focusing on public safety and advanced application development. It can further the development of smart cities and self-driving vehicles.
While slow to adopt technological trends, the healthcare industry also benefits from ML. Thanks to the technology, patients can rely on wearables and sensors that send real-time data about their health to their doctors. ML also lets doctors analyze healthcare trends to improve treatments and come up with diagnoses.
Much of the e-commerce boom could be attributed to ML advancements, as it allows customers to experience personalized shopping. Online stores use ML to analyze a person’s purchase history and come up with relevant recommendations. E-commerce startups also often rely on ML to gather and analyze data to create robust marketing campaigns.
Oil and Gas
ML has also been quite useful for the oil and gas sector. With it, engineers can use sensors to analyze ground minerals, predict failures, and streamline the overall oil distribution process to reduce costs.
Perhaps one of the most notable applications of ML is in the transportation industry. The sector relies heavily on developing routes and highways to ensure efficiency and reduce accidents. The modeling and data analysis elements of ML systems allow transportation companies to make better decisions when planning travel schedules and routes.
ML will continue to bloom in the years to come. And businesses that are taking advantage of its capabilities as early as now are bound to benefit greatly.