machine learning figure for each blurry image number is predictedonly the number is misinterpreted as then an ambiguous number is predicted to be but it could as well be even to human eyes this isn' clear with the bottom left number is ambiguouseven to humansis it or it' debatablebut the algorithm thinks it' by discerning which images were misinterpretedwe can train the model further by labeling them with the correct number they display and feeding them back into the model as new training set (step of the data science processthis will make the model more accurateso the cycle of learnpredictcorrect continues and the predictions become more accurate this is controlled data set we're using for the example all the examples are the same size and they are all in shades of gray expand that up to the variable size images of variable length strings of variable shades of alphanumeric characters shown in the captcha controland you can appreciate why model accurate enough to predict any captcha image doesn' exist yet in this supervised learning exampleit' apparent that without the labels associated with each image telling the program what number that image showsa model cannot be built and predictions cannot be made by contrastan unsupervised learning approach doesn' need its data to be labeled and can be used to give structure to an unstructured data set unsupervised learning it' generally true that most large data sets don' have labels on their dataso unless you sort through it all and give it labelsthe supervised learning approach to data won' work insteadwe must take the approach that will work with this data because we can study the distribution of the data and infer truths about the data in different parts of the distribution we can study the structure and values in the data and infer newmore meaningful data and structure from it many techniques exist for each of these unsupervised learning approaches howeverin the real world you're always working toward the research goal defined in the first phase of the data science processso you may need to combine or try different techniques before either data set can be labeledenabling supervised learning techniquesperhapsor even the goal itself is achieved