tist there have been many good books written about the theory of machine learningand if we were able to excite you about the possibilities that machine learning opens upwe suggest you pick up at least one of them and dig deeper we already mentioned hastietibshiraniand friedman' book the elements of statistical learning in the prefacebut it is worth repeating this recommendation here another quite accessible bookwith accompanying python codeis machine learningan algorithmic perspective by stephen marsland (chapman and hall/crctwo other highly recommended classics are pattern recognition and machine learning by christopher bishop (springer) book that emphasizes probabilistic frameworkand machine learninga probabilistic perspective by kevin murphy (mit press) comprehensive (read , pagesdissertation on machine learning methods featuring in-depth discussions of state-of-the-art approachesfar beyond what we could cover in this book other machine learning frameworks and packages while scikit-learn is our favorite package for machine learning and python is our favorite language for machine learningthere are many other options out there depending on your needspython and scikit-learn might not be the best fit for your particular situation often using python is great for trying out and evaluating modelsbut larger web services and applications are more commonly written in java or ++and integrating into these systems might be necessary for your model to be deployed another reason you might want to look beyond scikit-learn is if you are more interested in statistical modeling and inference than prediction in this caseyou should consider the statsmodel package for pythonwhich implements several linear models with more statistically minded interface if you are not married to pythonyou might also consider using ranother lingua franca of data scientists is language designed specifically for statistical analysis and is famous for its excellent visualization capabilities and the availability of many (often highly specializedstatistical modeling packages another popular machine learning package is vowpal wabbit (often called vw to avoid possible tongue twisting) highly optimized machine learning package written in +with command-line interface vw is particularly useful for large datasets and for streaming data for running machine learning algorithms distributed on clusterone of the most popular solutions at the time of writing is mlliba scala library built on top of the spark distributed computing environment andreas might not be entirely objective in this matter wrapping up