Frequently Asked Questions

An enormous amount of raw data can be transformed into meaningful information with machine learning. When implemented correctly, ML can serve as a solution to a variety of complex business problems and predict complex customer behavior.

Ml-Benefits

Frequently Asked Questions

Who is the mobile app available to?

The Ml Benefits OnLine iPhone app is available to customers who have access to Benefit.ml.com. There are some features and services that are not available in this app that are available on Benefit.ml.com. Visit the website to access all features.

What is Machine Learning?

Machine Learning (ML) is one of the most discussed and at the same time controversial topics in business today. While early adopters try to convince everyone that AI and related technologies will shape the future, more conservative CEOs and CTOs don’t jump to conclusions about risks and security issues. At the same time, many people without technical training can hardly distinguish all buzzwords like “machine learning”, “deep learning”, “artificial intelligence” and so on.

What is a machine learning algorithm?

In comparison with regular analytics algorithms, machine learning is characterized by its adaptability. This is the hallmark of the industry. ML algorithms become more accurate as they consume more data.

In which industry is machine learning widely used?

Machine learning is quickly becoming ubiquitous in every industry, from agriculture to medical research, to the stock market, traffic control, and more. For example, machine learning can be used in agriculture for various tasks such as weather forecasting and crop rotation.

What is machine learning for?

Machine Learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. ML algorithms learn iteratively from data, allowing computers to find different types of hidden information without being explicitly programmed to do so. ML is evolving so quickly and is mainly driven by new computer technologies.