choosing the right learning algorithm is not simply shootout of the three or four that are in your toolbox (there will be more algorithms in your toolbox that you will seeit is more of thoughtful process of weighing different performance and functional requirements do you need fast results and are willing to sacrifice qualityor would you rather spend more time to get the best possible resultdo you have clear idea of the future data or should you be bit more conservative on that sidefinallymeasuring the performance is the part where most mistakes are waiting for the aspiring ml learner there are easy onessuch as testing your approach with the same data on which you have trained but there are more difficult onesfor examplewhen you have imbalanced training data againdata is the part that determines whether your undertaking will fail or succeed we see that only the fourth point is dealing with the fancy algorithms neverthelesswe hope that this book will convince you that the other four tasks are not simply choresbut can be equally important if not more exciting our hope is that by the end of the book you will have truly fallen in love with data instead of learned algorithms to that endwe will not overwhelm you with the theoretical aspects of the diverse ml algorithmsas there are already excellent books in that area (you will find pointers in appendixwhere to learn more about machine learninginsteadwe will try to provide an intuition of the underlying approaches in the individual --just enough for you to get the idea and be able to undertake your first steps hencethis book is by no means "the definitive guideto machine learning it is more kind of starter kit we hope that it ignites your curiosity enough to keep you eager in trying to learn more and more about this interesting field in the rest of this we will set up and get to know the basic python librariesnumpy and scipyand then train our first machine learning using scikit-learn during this endeavorwe will introduce basic ml concepts that will later be used throughout the book the rest of the will then go into more detail through the five steps described earlierhighlighting different aspects of machine learning in python using diverse application scenarios what to do when you are stuck we try to convey every idea necessary to reproduce the steps throughout this book neverthelessthere will be situations when you might get stuck the reasons might range from simple typos over odd combinations of package versions to problems in understanding