Certificate in Core Text Clustering Concepts
-- ViewingNowThe Certificate in Core Text Clustering Concepts is a comprehensive course designed to equip learners with essential skills in text clustering. This certification program focuses on teaching the latest concepts, tools, and techniques for organizing and analyzing large text data sets.
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โข Introduction to Text Clustering: Defining text clustering, understanding its importance, and exploring primary use cases. โข Preprocessing Techniques: Data cleaning, tokenization, stopword removal, stemming, and lemmatization for text normalization. โข Feature Extraction: Identifying and extracting features from text data, including bag-of-words, TF-IDF, and word embeddings. โข Distance Metrics: Measuring similarities between text samples using Jaccard, Cosine, Euclidean, and Levenshtein distances. โข Hierarchical Clustering: Linkage methods, dendrograms, and cluster evaluation for hierarchical text clustering. โข Partitioning Methods: K-means, K-medoids, and DBSCAN algorithms for partition-based text clustering. โข Density-Based Clustering: Density-based spatial clustering of applications with noise (DBSCAN) and its applications. โข Evaluation Metrics: Understanding internal and external evaluation metrics for clustering performance assessment. โข Challenges & Best Practices: Addressing challenges in text clustering, such as noise, outliers, and high dimensionality.
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