Artificial Intelligence, data science, and machine learning – all fall in the same domain. The catch is which among them serves the right purpose given the situation. Over the years, we have seen the immense applications of data science, AI and ML in varied fields. The results delivered talk volumes about how efficient they are and how better can they be deployed in the coming years.

AI, being a replica of human intelligence aids in making better decisions by understanding data in-depth, identifying the patterns and trends which otherwise would have been difficult for humans to do the same manually. The thing with AI is that you need lots and lots of data to be able to understand the data. If you do not have a huge amount of data to deal with, the AI model would deliver results only for a small amount of data. In such a case, the accuracy of the prediction or decision could be low. Simply put, the more the amount of data, the better is the model trained to be able to deliver results with improved efficiency and accuracy. But the problem isn’t the availability of data for we know the amount of data generated on a day-to-day basis is humungous. The area of concern here is what to do when the model trained is deployed to work with new data? Will the model be successful in applying the knowledge gained to deal with new data sets? This is exactly where machine learning comes into play.

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