Machine Learning and Data Quality

#artificialintelligence 

Classic examples are television, where the data concerning the programmes you watch or display and interest in watching can allow the Machine Learning software to identify other shows you would like; and Facebook, where their Machine Learning programme works out which news items appear on your timeline based on your activity and commenting on the site. A basic and fundamental truth concerning Machine Learning is that the best designed computer algorithms and other things Machine Learning can do are only going to be as good as the data the Machine Learning software works with. Ambitious programmers who give their Machine Learning programmes large quantities of Big Data to work with are bound to be disappointed if the Machine Learning appears to have learnt nothing, or writes its own algorithms that then don't work, the cause being in the poor quality of the data, dirty and full of corruptions, mismatches, duplications and other inaccuracies. Spotless Data's unique web-based API solution to dirty data can be built into your Machine Learning software in its design or build phase or you can simply pass your data through our unique web-based API before entering it into the data lake or warehouse where the machine learning computer software will start to work with it, roducing the Machine Learning software and algritjms that will allow your company to stand out among its competitors and attract the lion's share of the pool of potential customers.