Even within the same type, for example classification, there are several algorithms available. The type of algorithms that you apply is based largely on your domain knowledge. Once the data is ready, you would apply various Machine Learning algorithms such as classification, regression, clustering and so on to solve the problem at your end. In short, your big data needs lots of preprocessing before it can be used for Machine Learning. The irrelevant data columns or ‘features’ as termed in Machine Learning terminology, must be removed before the data is fed into a machine learning algorithm.
To train the machine to analyze big data, you need to have several considerations on the data −īesides, not all the columns in the data table would be useful for the type of analytics that you are trying to achieve. The foundation of any Machine Learning application is data - not just a little data but a huge data which is termed as Big Data in the current terminology.
0 kommentar(er)
