5 August 2019 1:17pm
Are you ready to unlock the magic of machine learning?
Pattern recognition just one part of machine learning
- Supervised machine learning—These human-trained algorithms are given tagged ‘training’ data and an expected output. The result is a rules-based, decision-making process that enables the machine learning algorithm to forecast on new and existing data. Examples are regression analysis and predictive modeling.
- Unsupervised machine learning—These self-taught algorithms are designed to gather, classify and categorize data. Subsequently, decisions are made on the data. Unsupervised machine learning, such as clustering, is used to solve very complex problems.
- Semi-supervised machine learning—These algorithms fall somewhere in between supervised and unsupervised learning. They use both labelled and unlabelled data for training.
- Reinforcement learning— This variety of machine learning takes cues from behavior psychology. Used for game theory and simulation optimization, reinforcement learning facilitates actions based on rewards for previous actions.
Companies are racing to take advantage of machine learning
- Conducting sentiment analysis of news related to stocks
- Identifying trading signals from a range of news, financial and legal data
- Detecting fraud by spotting patterns and using predictive analytics
- Using natural language processing to stay alert to and interpret new regulations
- Automate compliance checks to mitigate money laundering risk
- Learn how Nexis® Data as a Service can help power your machine learning algorithms.
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