Machine Learning Algorithms
What are machine learning algorithms?
Before we can define machine learning algorithms, we must first provide an introduction to machine learning. Basically, machine learning is a computer’s ability to learn and solve problems without someone explicitly programming it. Machine learning studies the algorithms and mathematical models that computer systems use to improve, step by step, their performance of a particular task. It’s based on the notion that systems can learn from data and information, find patterns and autonomously make decisions with little human intervention.
Machine learning algorithms are the processes and rules a computer follows for solving a specific problem. These algorithms receive and analyse data to predict outcomes within a satisfactory range. As the algorithms receive additional data, they become ‘smarter’ over time, learning and optimising their actions to improve performance.
Machine learning algorithms fall into four main categories:
- supervised
- semisupervised
- unsupervised
- reinforcement
Supervised learning
With supervised learning, the computer learns by example. A human feeds the machine learning algorithm a known dataset that includes desired inputs and outputs, and the algorithm must figure out a way to arrive at those inputs and outputs. The algorithm finds patterns in the data, learns from observations and makes predictions, with the human correcting the computer along the way. This continues until the algorithm achieves a high degree of accuracy.
Semisupervised learning
Semisupervised learning employs both labelled and unlabelled data. Labelled data is basically information that has been tagged so the algorithm can understand it, whereas unlabelled data doesn’t have such tags. By using both, the machine learning algorithms can learn to label unlabelled data.
Unsupervised learning
With unsupervised learning, the machine learning algorithm examines data to pinpoint patterns without the aid of a human. The computer determines connections and relationships by analysing the available data. The machine learning algorithm must autonomously interpret large chunks of data and deal with it accordingly. It attempts to give the data organisation and structure. As the algorithm evaluates more data, its decision-making capability progressively improves.
Reinforcement learning
Nexis Data as a Service
You can optimise your processes with machine learning, and Nexis Data as a Service (DaaS) can help.
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Nexis DaaS comes in a range of APIs to suit your organisation’s specific content needs, technical capabilities and risk-mitigation workflow. With these APIs, you can search and retrieve data from LexisNexis servers using your proprietary, in-house business applications or a LexisNexis-approved third-party software solution. Alternatively, you can host bulk downloaded content on your own servers for use in data mining, machine learning and artificial intelligence applications.
Learn more about how Nexis DaaS can help you make more confident, data-driven decisions.
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