Category: Machine Learning

Machine learning (ML) is a branch of research concerned with understanding and developing methods that ‘learn’ that is, methods that use data to improve performance on a set of tasks. It is regarded as a component of artificial intelligence.

ML algorithms construct a model from sample data, referred to as training data. The purpose is to make predictions or decisions without being explicitly programmed to do so. Such algorithms are used in a wide range of applications, including medicine, email filtering, speech recognition, and computer vision, where developing traditional algorithms to perform the required tasks would be difficult or impossible. 

However, not all machine learning is statistical learning. A subset of this field is closely related to computational statistics. Computational statistics focuses on making predictions using computers, but not all machine learning is statistical learning.

Mathematical optimization research provides methods, theory, and application domains to the field of machine learning. A related field of study is data mining, which focuses on exploratory data analysis via unsupervised learning. Some ML implementations use data and neural networks to mimic the operation of a biological brain. ML is also known as predictive analytics when applied to business problems.

Without being explicitly programmed, m.l. programs can perform tasks. It entails computers learning from provided data in order to perform specific tasks.

For simple tasks assigned to computers, it is possible to program algorithms that instruct the machine on how to execute all steps required to solve the problem at hand; no learning is required on the computer’s part. It can be difficult for a human to manually create the necessary algorithms for more advanced tasks. In practice, it may be more effective to assist the machine in developing its own algorithm rather than having human programmers specify each required step.