Angielski
The well-known Machine Learning systems generally use a power and resources of only one personal computer. Nowadays, new devices, social media, and other sources generate the data of huge volumes. More innovative technologies which would be need for big data analysis. The selection of the strategy depends on the volume of data analysed. When we deal with a large data set, the well-known data mining systems usually are used. The complex problems of data analysis require usage of parallel and distributed computing based systems and technologies. Big data initiate development of new technologies. Hadoop based technologies and libraries are the most popular solutions for big data analysis and clustering. Machine learning is ideal for exploiting the opportunities hidden in big data. It delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing data sets.
