Big data enables machine learning models to extensively analyze and regulate risks. For fraud detection, American Express applies machine learning to analyze large historical datasets.

How do Machine Learning algorithms handle such large amount of data? This question was originally answered on Quora by Håkon Hapnes Strand.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Supervised machine learning algorithms, especially classification algorithms, have been widely used in data analysis of industrial big data. Among them, the support vector machine (SVM) has achieved ...

Machine learning models are essentially trained with algorithms; they are generated when algorithms are applied to a specific given data set. While algorithms are simply general approaches to ...

Machine learning-based classification models have improved accuracy by combining the results of multiple ML algorithms. Such an ensemble approach has been explored widely in various application ...

The utility of this Machine Learning model extends beyond classifying infrastructure types. It can be leveraged in broader humanitarian and development contexts. For this reason, the UNDP Country ...

In the recent past, water quality classification has gained the attention of researchers in Machine Learning. This has been attributed to the advancement of computing resources and the availability of ...

Machine learning depends on a number of algorithms for turning a data set into a model. Which algorithm works best depends on the kind of problem you’re solving, the computing resources ...