binarizing data to binarize given feature setwe make use of threshold if any value within given dataset is greater than the thresholdthe value is replaced by if the value is less than the threshold we will replace itresults preprocessing binarizer( fit(datatransform(dataprint(resultsan instance of binarizer is created with the argument is the threshold that will be used in the binarizing algorithm[ ]all values in the data that are less than will have in their stead the opposite also holds true machine learning machine learning is subfield of artificial intelligence we know that we can never truly create machines that actually "thinkbut we can supply machines with enough data and models by which sound judgment can be reached machine learning focuses on creating autonomous systems that can continue the process of decision makingwith little or no human intervention in order to teach the machinewe need data drawn from the real world for instanceto shift through which -mails constitute spam and which ones don'twe need to feed the machine with samples of each after obtaining this datawe have to run the data through models (algorithmsthat will use probability and statistics to unearth patterns and structure from the data if this is properly donethe algorithm by itself will be able to analyze -mails and properly categorize them sorting -mails is just one example of what machines can do if they are "trained