Gensim
Gensim is an open-source Python library for unsupervised topic modeling and natural language processing (NLP). It specializes in extracting semantic topics from documents using algorithms like Latent Dirichlet Allocation (LDA) and word embeddings like Word2Vec. The library is designed to handle large text collections efficiently with memory-friendly streaming and incremental training.
Developers should learn Gensim when working on NLP projects that require topic modeling, document similarity analysis, or word vector representations, such as in content recommendation systems, document clustering, or semantic search engines. It's particularly useful for processing large corpora where scalability and performance are critical, as it supports out-of-core algorithms that don't require loading all data into memory at once.