This course begins by performing basic cleaning techniques for textual data. You will evaluate latent Dirichlet allocation models and execute non-negative matrix factorization models. Finally, you will interpret the results of topic models and identify the best topic model for the given scenario. You will see how topic modelling provides insights into the underlying structure of documents. By the end of this course, you will be able to build fully functioning topic models to derive value and insights for your business
Previous experience of using Python is required. Prior knowledge of machine learning is not necessary. It's assumed that the learner is aware of the basic mathematical notations and has worked with arrays and dataframes in Python.