the universal workflow of machine learning in this sectionwe'll present universal blueprint that you can use to attack and solve any machine-learning problem the blueprint ties together the concepts you've learned about in this problem definitionevaluationfeature engineeringand fighting overfitting defining the problem and assembling dataset firstyou must define the problem at handwhat will your input data bewhat are you trying to predictyou can only learn to predict something if you have available training datafor exampleyou can only learn to classify the sentiment of movie reviews if you have both movie reviews and sentiment annotations available as suchdata availability is usually the limiting factor at this stage (unless you have the means to pay people to collect data for youwhat type of problem are you facingis it binary classificationmulticlass classificationscalar regressionvector regressionmulticlassmultilabel classificationsomething elselike clusteringgenerationor reinforcement learningidentifying the problem type will guide your choice of model architectureloss functionand so on you can' move to the next stage until you know what your inputs and outputs areand what data you'll use be aware of the hypotheses you make at this stageyou hypothesize that your outputs can be predicted given your inputs you hypothesize that your available data is sufficiently informative to learn the relationship between inputs and outputs until you have working modelthese are merely hypotheseswaiting to be validated or invalidated not all problems can be solvedjust because you've assembled examples of inputs and targets doesn' mean contains enough information to predict for instanceif you're trying to predict the movements of stock on the stock market given its recent price historyyou're unlikely to succeedbecause price history doesn' contain much predictive information one class of unsolvable problems you should be aware of is nonstationary problems suppose you're trying to build recommendation engine for clothingyou're training it on one month of data (august)and you want to start generating recommendations in the winter one big issue is that the kinds of clothes people buy change from season to seasonclothes buying is nonstationary phenomenon over the scale of few months what you're trying to model changes over time in this casethe right move is to constantly retrain your model on data from the recent pastor gather data at timescale where the problem is stationary for cyclical problem like clothes buyinga few yearsworth of data will suffice to capture seasonal variation--but remember to make the time of the year an input of your modellicensed to