Masterclass Certificate in Text Clustering with R
-- ViewingNowThe Masterclass Certificate in Text Clustering with R is a comprehensive course designed to equip learners with essential skills in text analysis and clustering. This course is critical for individuals seeking to advance their careers in data science, business intelligence, and research fields.
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⢠Introduction to Text Clustering with R – covering basic concepts, benefits, and applications of text clustering, as well as an overview of the R programming language and its relevant libraries for text clustering.
⢠Data Preprocessing – focusing on data cleaning, tokenization, stopwords removal, and stemming/lemmatization to prepare text data for clustering.
⢠Similarity Measures – delving into various similarity measures used in text clustering, such as Jaccard, Cosine, Euclidean, and Manhattan distances.
⢠Text Clustering Algorithms – presenting an in-depth analysis of popular text clustering algorithms, including K-means, Hierarchical, DBSCAN, and Spectral Clustering.
⢠Evaluation Metrics – discussing various evaluation metrics to assess text clustering performance, such as Purity, Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Silhouette Coefficient.
⢠Dimensionality Reduction – covering various dimensionality reduction techniques, such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-Negative Matrix Factorization (NMF), to improve clustering performance.
⢠Advanced Topics in Text Clustering – exploring advanced topics, such as incremental text clustering, semi-supervised text clustering, and hierarchical text clustering.
⢠Real-World Applications – showcasing real-world applications of text clustering, such as customer segmentation, topic modeling, and social media analytics.
⢠Best Practices in Text Clustering – discussing best practices in text clustering, including selecting the right clustering algorithm, tuning hyperparameters, and addressing common challenges.
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