fundamentals of machine learning summary define the problem at hand and the data on which you'll train collect this dataor annotate it with labels if need be choose how you'll measure success on your problem which metrics will you monitor on your validation datadetermine your evaluation protocolhold-out validationk-fold validationwhich portion of the data should you use for validationdevelop first model that does better than basic baselinea model with statistical power develop model that overfits regularize your model and tune its hyperparametersbased on performance on the validation data lot of machine-learning research tends to focus only on this step--but keep the big picture in mind licensed to