of coursewinning something is nicebut you can gain lot of useful experience just by participating soyou have to stay tunedespecially after the competition is over and participants start sharing their approaches in the forum most of the timewinning is not about developing new algorithmit is about cleverly preprocessingnormalizingand combining the existing methods what was left out we did not cover every machine learning package available for python given the limited spacewe chose to focus on scikit-learn howeverthere are other optionsand we list few of them heremodular toolkit for data processing (mdp at pybrain at machine learning toolkit (milkat sourceforge net milk degdeg this package was developed by one of the authors of this bookand covers some algorithms and techniques that are not included in scikit-learn more general resource is at source machine learning software as is usually the case with repositories such as this onethe quality varies between excellentwell-maintained software and projects that were one-offs and then abandoned it may be worth checking out if your problem is very specific and none of the more general packages address it summary we are now truly at the end we hope you have enjoyed the book and feel well equipped to start your own machine learning adventure we also hope you have learned the importance of carefully testing your methodsin particularof using correct cross-validation and not reporting training test resultswhich are an over-inflated estimate of how good your method really is