University · Data Science · Natural Language Processing for Data Science
Classification, Sentiment Analysis, and Topic Modeling
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Text classification (Naive Bayes, SVM, neural), sentiment analysis approaches, LDA topic modeling, named entity recognition, text summarization
Inhaltsübersicht
- Text Classification with Traditional and Neural Methods
- Sentiment Analysis Approaches and Challenges
- Topic Modeling with Latent Dirichlet Allocation
- Named Entity Recognition and Text Summarization
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- Text Preprocessing and Representation
- Large Language Models and NLP Applications
- Text Preprocessing: Tokenization, Stemming, Lemmatization, and TF-IDF
- Word Embeddings: Word2Vec, GloVe, and Contextual Representations
- Large Language Models: Transformers, BERT, and GPT Architecture
- Sentiment Analysis, Named Entity Recognition, and Text Classification
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