with images of sharks labeled as fish and images of oceans labeled as water by being trained on this datathe supervised learning algorithm should be able to later identify unlabeled shark images as fish and unlabeled ocean images as water common use case of supervised learning is to use historical data to predict statistically likely future events it may use historical stock market information to anticipate upcoming fluctuationsor be employed to filter out spam emails in supervised learningtagged photos of dogs can be used as input data to classify untagged photos of dogs unsupervised learning in unsupervised learningdata is unlabeledso the learning algorithm is left to find commonalities among its input data as unlabeled data are more abundant than labeled datamachine learning methods that facilitate unsupervised learning are particularly valuable the goal of unsupervised learning may be as straightforward as discovering hidden patterns within datasetbut it may also have goal of feature learningwhich allows the computational machine to automatically discover the representations that are needed to classify raw data unsupervised learning is commonly used for transactional data you may have large dataset of customers and their purchasesbut as human you will likely not be able to make sense of what similar attributes can be drawn from customer profiles and their types of purchases with this data fed into an unsupervised learning algorithmit may be determined that women of certain age range who buy unscented soaps are likely to be pregnantand therefore marketing