Machine Learning Definition, Types, Applications, Trends

An example from the financial industry can better explain how machine learning works. Financial researchers, analysts, asset managers, and individual investors in the securities market have always combed through a large amount of data from various companies throughout the world in order to make profitable investment selections. However, some important information may not be extensively broadcast by the media and may only be known to a select few who are workers of the company or inhabitants of the country from whence the information originates. Furthermore, humans can only collect and process so much data in a given amount of time. This is where artificial intelligence (AI) comes in.

Machine learning could be used by an asset management organization for investment analysis and research. Assume the asset manager focuses solely on mining stocks. The system’s model scans the web for all forms of news events from firms, industries, cities, and countries, and the data set is made up of this information. With their human abilities and intellects, the firm’s asset managers and researchers would not have been able to extract the information from the data collection. Only data regarding mining businesses, regulatory rules on the exploration sector, and political events in specific countries are extracted from the data set by the parameters generated alongside the model.

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