Latent Dirichlet Allocation (LDA) is a probabilistic topic modeling technique used to discover hidden themes in large collections of text data. It enables organizations to automatically group documents into topics based on word distributions and statistical patterns. This training explains core concepts such as topics, words, document-topic distribution, and word-topic probability modeling. It also covers how LDA works using Bayesian inference, Dirichlet priors, and iterative optimization techniques. You will learn how LDA is applied in text mining, content classification, recommendation systems, and information retrieval. The course also highlights best practices for preparing text data, tuning models, and interpreting topic outputs effectively.
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