LDA
Latent Dirichlet Allocation (LDA) is a generative probabilistic model used for topic modeling in natural language processing and machine learning. It assumes that documents are mixtures of topics, where each topic is a distribution over words, and automatically discovers hidden thematic structures in large text corpora. LDA is widely applied for document classification, information retrieval, and content recommendation by extracting interpretable topics without prior labeling.
Developers should learn LDA when working on text analysis projects, such as building recommendation systems, analyzing customer feedback, or organizing large document collections, as it helps uncover latent patterns and reduce dimensionality. It is particularly useful in data science, NLP applications, and academic research where unsupervised learning and topic discovery are required, enabling insights from unstructured text data.