tuning the instance tuning the classifier fetching the data slimming the data down to chewable chunks preselection and processing of attributes defining what is good answer creating our first classifier starting with the -nearest neighbor (knnalgorithm engineering the features training the classifier measuring the classifier' performance designing more features deciding how to improve bias-variance and its trade-off fixing high bias fixing high variance high bias or low bias using logistic regression bit of math with small example applying logistic regression to our postclassification problem looking behind accuracy precision and recall slimming the classifier ship itsummary classification ii sentiment analysis sketching our roadmap fetching the twitter data introducing the naive bayes classifier getting to know the bayes theorem being naive using naive bayes to classify accounting for unseen words and other oddities accounting for arithmetic underflows creating our first classifier and tuning it solving an easy problem first using all the classes tuning the classifier' parameters cleaning tweets taking the word types into account determining the word types iii