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Machine Learning

Machine learning (ML) is a subcategory of artificial intelligence that refers to the process by which PCs develop pattern recognition, or the ability to continually learn and make predictions based on data, after which they make adjustments without being specifically programmed.

How does Machine Learning Work?

Machine learning is incredibly complex, and how it works varies depending on the task and the algorithm used to accomplish it. An ML model is a PC analyzing data and identifying patterns, then using that insight to better complete the assigned task. Any task based on a set of data points or norms can be automated using machine learning, even the most complex tasks like answering customer service calls and reviewing resumes.

Why is Machine Learning Important?

The revival of interest in machine-based learning is due to the same features that have made data mining and Bayesian analysis more standard than ever. Things like the increasing volumes and varieties of data available, cheaper and more powerful computational processing, and affordable data storage.

All these things mean that it’s possible to fast and automatically produce models that can analyze more extensive, more complex data and make faster, more accurate results – even on a vast scale. And with precise model building, an association has a well chance of identifying profitable opportunities – or avoiding unknown risks.

Who Uses it?

Most industries with large amounts of data have known the value of ML technology. By gaining insights from this data – often in real-time – organizations can work more efficiently or gain an advantage over their competitors.

Financial Services

Banks and other companies in the financial industry use ML technology for two primary purposes: to identify important insights into data and to prevent fraud. Senses can recognize investment opportunities or help investors know when to buy or sell. Data mining can also identify customer with high-risk profiles or use cyber surveillance to spot warning signs of fraud.


Government agencies like public safety and utilities need machines because they have multiple data sources from which insights can be drawn. For example, sensor data analysis identifies ways to increase efficiency and save money. In addition, machine-based learning can help detect fraud and minimize identity theft.

Health Care

Machine learning is a rapidly growing trend in the healthcare industry, thanks to the emergence of wearable strategies and sensors that can use data to evaluate a patient’s health in real-time. Additionally, technology can help medical experts analyze data to identify styles or red flags that may lead to improved diagnosis and treatment.

Marketing and Sales

Websites that recommend items you might like based on past purchases use machine learning to analyze your purchase history – and promote other things that might interest you. The future of retail is the ability to capture data, study it, and use it to personalize a shopping understanding (or implement a marketing campaign).

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