Machine learning applied to data science
Machine learning has become increasingly important for data science analysis as it has been for a multitude of other fields. A defining characteristic of machine learning is the ability of a model to be trained on a set of representative data and then later used to solve similar problems. There is no need to explicitly program an application to solve the problem. A model is a representation of the real-world object.
For example, customer purchases can be used to train a model. Subsequently, predictions can be made about the types of purchases a customer might subsequently make. This allows an organization to tailor ads and coupons for a customer and potentially providing a better customer experience.
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Training can be performed in one of several different approaches:
- Supervised learning: The model is trained with annotated, labeled, data showing corresponding correct results
- Unsupervised learning: The data does not contain results, but the model is expected...
