Machine learning is a type of technology which is increasingly being applied within a variety of systems, products and services lately. Indeed, the phrase itself was first defined by computer scientist Arthur Samuel in 1959. He described machine learning as a branch of computer science which regards how machines solves problems without being explicitly programmed to do so: an ability which is fundamentally different from machines which have been pre-programmed with a certain set of functions.
Machine learning is essentially defined by its analysis of large bundles of data, and its ability to draw useful conclusions from them. These conclusions are used to then complete the tasks in the most effective and dynamic way possible, mainly through using nonlinear reasoning (The same type of reasoning used by humans).
Machine learning is essentially a form of reasoning which gives a computer system the ability to logically draw conclusions based on the data provided to it. It is a broad concept, and is not applied to one scientific discipline; rather, it covers various aspects of computing, such as Language Processing, meaning the ability for a system to comprehend and interpret the deeper subtleties and ‘context’ which is implicit within human language. Moreover, this type of ‘Deep Learning’ primarily aims to allow for a machine system to mimic the thinking style of a human brain using artificial neural networks.
Such systems have algorithms that essentially function as neurons, working together in understanding features and patterns within data sets. Unlike other machine based systems that are designed to accomplish specific tasks, deep learning entails that machines are programmed with a more complex set of capabilities related to studying and classifying data.
Indeed, machine learning, specifically deep learning, is suitable for calculating data of a more subtle (Perhaps even illogical) nature, such as language, sounds or images. Machine Learning can provide a precise, context-based analysis of such things, and form conclusions.
For example, an e-commerce business that sells shirt will generally be limited within certain size variations, mainly ‘small’ to ‘large.’ When applying cognitive computing, such systems can analyze the various preferences obtained from the customer, and automatically suggest an appropriate size. Further, a system aimed at taxi reservations has many factors that must be considered, such as how to communicate with customers, and their intended location. Machine learning can be applied to analyze such information so as to ensure the right conclusions are reached.
The main factor needed here is large amounts of data to analyze. This is so that a program utilizing machine learning can cross reference this data so as to learn specific patterns and create a model of estimates. Essentially, the more data which is used, the cleverer the machine’s ‘cognitive’ abilities will be.