in the previous we clustered texts into groups this is very useful toolbut it is not always appropriate clustering results in each text belonging to exactly one cluster this book is about machine learning and python should it be grouped with other python-related works or with machine-related worksin the paper book agea bookstore would need to make this decision when deciding where to stock it in the internet store agehoweverthe answer is that this book is both about machine learning and pythonand the book can be listed in both sections we willhowevernot list it in the food section in this we will learn methods that do not cluster objectsbut put them into small number of groups called topics we will also learn how to derive between topics that are central to the text and others only that are vaguely mentioned (this book mentions plotting every so oftenbut it is not central topic such as machine learning isthe subfield of machine learning that deals with these problems is called topic modeling latent dirichlet allocation (ldalda and ldaunfortunatelythere are two methods in machine learning with the initials ldalatent dirichlet allocationwhich is topic modeling methodand linear discriminant analysiswhich is classification method they are completely unrelatedexcept for the fact that the initials lda can refer to either howeverthis can be confusing scikit-learn has submodulesklearn ldawhich implements linear discriminant analysis at the momentscikit-learn does not implement latent dirichlet allocation the simplest topic model (on which all others are basedis latent dirichlet allocation (ldathe mathematical ideas behind lda are fairly complexand we will not go into the details here