This talk covers how one can find the latent topics from a bunch of documents without any labels (unsupervised learning). Also covered are Latent Dirichlet Allocation (LDA), a type of document clustering model. LDA can be used for multiple NLP pipelines, eg; Document clustering, topic evaluation, feature extraction, Document similarity study, text summarisation etc. Evaluating the quality of result from such unsupervised models are a challenge, we will discuss few such effective evaluation methods.