In recent years, such terms as “artificial intelligence” (AI) and “machine learning” (ML) have often been used outside the scientific community. The reason is that the area of practical application of these technologies extends to our daily life year by year. As a down-to-earth person, I find this topic very interesting. Thus, I decided to learn more about the machine learning definition and find out why this study engages so much public attention.
Machine learning is a subset of artificial intelligence. Its main feature is the ability to automatically learn models from data. This branch of science is closely related to mathematics, statistics, and data analysis. What is important to know about machine learning is that it has many examples of practical application:
- analysis of past events in datasets;
- search for patterns and correlations with certain conditions;
- making decisions and conclusions depending on a given algorithm.
ML technology is currently used in cell-phones for the purpose of face recognition (Face ID). Neural processors in iPhones contain an algorithm that analyzes around 30,000 invisible dots on your face and remembers them to create your face map. Later, when the cell-phone is turned on, the camera scans your face. If it’s a match, the device gets unlocked.
Machine Learning in Football Analytics
The given example of ML application is so common that many people sometimes don’t even realize they deal with it every day. Another interesting way of using artificial intelligence is ML-based forecasting. Any soccer fan will be happy to know that machine learning has taken sports analytics to a whole new level of evolution. A vivid example of this is the recently launched project BPRO EXPERT that provides mathematical football predictions.
What makes machine-based forecasts special is that humans cannot influence them. On this page https://bproexpert.com/analytics/today you can find analytics for the upcoming matches in the world’s top championships. These are football predictions with an average accuracy of more than 80%. All of them are generated with the help of AI and machine learning models. Based on the data of past matches and a complex system of individual ratings, the program creates predictions relevant to the current market.
This all means that ML algorithms that can predict the results of sports competitions already exist. With a sufficient number of reliable datasets and a correctly compiled analysis algorithm, machine intelligence is able to accurately predict future events. People can only use the new opportunities that have opened up to them.