Machine learning is in the ascendancy. Particularly, when it comes to pattern recognition, machine learning is the method of choice. Tangible examples of its applications include fraud detection, image recognition, predictive maintenance, and train delay prediction systems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not only these).
Data Quality — Data from multiple sources across multiple time frames can be difficult to collate into clean and coherent data sets that will yield the maximum benefit from machine learning. Typical issues include missing data, inconsistent data values, autocorrelation, and so forth.