In computer science, machine learning refers to a type of data analysis that uses algorithms that learn from data. It is a type of artificial intelligence (AI) that provides systems with the ability to learn without being explicitly programmed. This enables computers to find data within data without human intervention.
What is important to know about machine learning is that data is being used to make predictions, not code. Data is dynamic so machine learning allows the system to learn and evolve with experience and the more data that is analyzed.
Origins of the Phrase Machine Learning
Machine learning was first defined in 1959 by Arthur Samuel, a pioneer in the field of artificial intelligence and machine learning. Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.
Supervised Versus Unsupervised Machine Learning
Typically, machine learning is categorized as supervised or unsupervised machine learning:
Supervised Machine Learning: A pre-defined set of examples are used to reach a conclusion whn given data.
Unsupervised Machine Learning: The system finds patterns and relationships in the data with no examples from which to draw conclusions.
Examples of Machine Learning
Today, machine learning algorithms can apply complex calculations to big data, very quickly. One of the most well-known examples of machine learning today is Google’s self-driving car. This driverless car relies heavily on machine learning and data mining to process all the sensor data.
Machine learning is also used in Web search engines, recommendation systems, online ad placement, email spam filters and many other applications.
Machine learning may be abbreviated as ML.
