Natural Language Processing - Course Details
Natural Language Processing enables machines to understand and generate human language. Learn text processing, sentiment analysis, and language models.
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What you'll learn?
- Text preprocessing and tokenization
- Word embeddings
- Sentiment analysis
- Named entity recognition
- Transformers and LLMs
Requirements
- Python programming
- Machine learning basics
- Understand fundamentals of Natural Language Processing (NLP)
- Tokenization
- Stop word removal
- Stemming and Lemmatization
- Lowercasing, punctuation removal, and normalization
- N-gram models and language modeling fundamentals
- Bag of Words (BoW)
- TF-IDF (Term Frequency–Inverse Document Frequency)
- Word2Vec
- FastText
- Contextual embeddings (BERT, GPT-style embeddings)
- BERT, GPT, RoBERTa, T5, and other transformer architectures
- Transformer fine-tuning and transfer learning
- NLTK
- SpaCy
- Sentiment Analysis
- Text Classification
- Named Entity Recognition (NER)
- Part-of-Speech (POS) tagging
- Topic Modeling (LDA, NMF)
- Chatbots and conversational AI
- Question Answering (QA) systems
- Text summarization
- Machine Translation
- Accuracy, Precision, Recall, F1-score
- BLEU, ROUGE, METEOR for text generation tasks
- Sentiment classifier for product reviews
- Rule-based and transformer-based chatbot
- News topic classification
- Text summarizer or translator
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