in and kaggle was dominated by two approachesgradient boosting machines and deep learning specificallygradient boosting is used for problems where structured data is availablewhereas deep learning is used for perceptual problems such as image classification practitioners of the former almost always use the excellent xgboost librarywhich offers support for the two most popular languages of data sciencepython and meanwhilemost of the kaggle entrants using deep learning use the keras librarydue to its ease of useflexibilityand support of python these are the two techniques you should be the most familiar with in order to be successful in applied machine learning todaygradient boosting machinesfor shallowlearning problemsand deep learningfor perceptual problems in technical termsthis means you'll need to be familiar with xgboost and keras--the two libraries that currently dominate kaggle competitions with this book in handyou're already one big step closer licensed to