machine learning blaze --blaze gives you data structures that can be bigger than your computer' main memoryenabling you to work with large data sets dispy and ipcluster --these packages allow you to write code that can be distributed over cluster of computers pp --python is executed as single process by default with the help of pp you can parallelize computations on single machine or over clusters pydoop and hadoopy--these connect python to hadoopa common big data framework pyspark--this connects python and sparkan in-memory big data framework now that you've seen an overview of the available librarieslet' look at the modeling process itself the modeling process the modeling phase consists of four steps feature engineering and model selection training the model model validation and selection applying the trained model to unseen data before you find good modelyou'll probably iterate among the first three steps the last step isn' always present because sometimes the goal isn' prediction but explanation (root cause analysisfor instanceyou might want to find out the causes of speciesextinctions but not necessarily predict which one is next in line to leave our planet it' possible to chain or combine multiple techniques when you chain multiple modelsthe output of the first model becomes an input for the second model when you combine multiple modelsyou train them independently and combine their results this last technique is also known as ensemble learning model consists of constructs of information called features or predictors and target or response variable your model' goal is to predict the target variablefor exampletomorrow' high temperature the variables that help you do this and are (usuallyknown to you are the features or predictor variables such as today' temperaturecloud movementscurrent wind speedand so on the best models are those that accurately represent realitypreferably while staying concise and interpretable to achieve thisfeature engineering is the most important and arguably most interesting part of modeling for examplean important feature in model that tried to explain the extinction of large land animals in the last , years in australia turned out to be the population number and spread of humans engineering features and selecting model with engineering featuresyou must come up with and create possible predictors for the model this is one of the most important steps in the process because model