Certificate in Core Topic Modeling Concepts

• Introduction to Topic Modeling: Understanding the basics and importance of topic modeling, differentiating it from other text mining techniques. • Probabilistic Graphical Models: Exploring directed graphs, plate notation, and modeling dependencies in topic models. • Latent Dirichlet Allocation (LDA): Delving into the most popular topic modeling algorithm, its components, and use cases. • Implementing LDA with Gensim: Hands-on experience using Python's Gensim library to create LDA models. • Evaluation Metrics for Topic Models: Learning how to assess topic model performance using coherence scores and other metrics. • Non-Negative Matrix Factorization (NMF): Investigating an alternative topic modeling algorithm and its applications. • Hierarchical Dirichlet Process (HDP): Examining an advanced topic modeling technique for modeling an unknown number of topics. • Real-world Applications: Practicing topic modeling on diverse datasets, from scientific literature to social media posts. • Data Preprocessing for Topic Modeling: Cleaning, normalizing, and formatting text data for optimal topic modeling outcomes.