what the book will teach you (and what it will notthis book will give you broad overview of the types of learning algorithms that are currently used in the diverse fields of machine learning and what to watch out for when applying them from our own experiencehoweverwe know that doing the "coolstuff--using and tweaking machine learning algorithms such as support vector machines (svm)nearest neighbor search (nns)or ensembles thereof--will only consume tiny fraction of the overall time of good machine learning expert looking at the following typical workflowwe see that most of our time will be spent in rather mundane tasks reading the data and cleaning it exploring and understanding the input data analyzing how best to present the data to the learning algorithm choosing the right model and learning algorithm measuring the performance correctly when talking about exploring and understanding the input datawe will need bit of statistics and basic math but while doing thisyou will see that those topicswhich seemed so dry in your math classcan actually be really exciting when you use them to look at interesting data the journey begins when you read in the data when you have to face issues such as invalid or missing valuesyou will see that this is more an art than precise science and very rewarding oneas doing this part right will open your data to more machine learning algorithmsand thus increase the likelihood of success with the data being ready in your program' data structuresyou will want to get real feeling of what kind of animal you are working with do you have enough data to answer your questionsif notyou might want to think about additional ways to get more of it do you maybe even have too much datathen you probably want to think about how best to extract sample of it often you will not feed the data directly into your machine learning algorithm insteadyou will find that you can refine parts of the data before training many timesthe machine learning algorithm will reward you with increased performance you will even find that simple algorithm with refined data generally outperforms very sophisticated algorithm with raw data this part of the machine learning workflow is called feature engineeringand it is generally very exciting and rewarding challenge creative and intelligent that you areyou will immediately see the results [