intermediate~5h
Text Classification and Clustering
Applied text classification using zero-shot generative models, embedding+classifier pipelines, and fine-tuned encoders; plus unsupervised text clustering and topic modeling with K-Means, HDBSCAN, and BERTopic — all grounded in embedding space.
2
Subtopics
2
Exercises
1
Projects
5
Quiz Qs
4
Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Choose between zero-shot generative classification, embedding+classifier, and fine-tuned encoder approaches based on label count and data availability
- •Build a text classifier by extracting embeddings and training a logistic regression or SVM head
- •Explain why cosine similarity in embedding space enables zero-shot and few-shot classification
- •Run K-Means and HDBSCAN clustering on a document corpus and interpret the clusters
- •Apply BERTopic to discover latent topics in a large unstructured text collection
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📂 Subtopics
Related concepts
cosine similarityembedding spaceUMAPHDBSCANK-MeansBERTopicc-TF-IDFlogistic regressionzero-shot classificationfew-shot learningsentence transformerssemantic search
Next to learn
embedding-model-finetuning →rag-workflow →semantic-search →