Natural Language Processing (NLP) - Foundation and Hands-on Projects
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๐ฅ In this full NLP Foundations course, we go from โWhat is NLP?โ all the way to a production-style, end-to-end pipeline for the travel & hospitality domain. Step by step, youโll clean and normalize text, extract entities and intents with spaCy, turn documents into vectors, run sentiment analysis with VADER, build Naive Bayes classifiers, explore topic models (NMF, LDA, BERTopic, Top2Vec), and finally glue everything โฆ
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Chapters (7)
1. NLP Foundations & Text Cleaning (pandas + NLTK)
44:08
2. spaCy Pipelines & Rule-Based Extraction (Matcher, EntityRuler)
55:09
3. Vectorizing Text with CountVectorizer, TFโIDF & Cosine Similarity
1:11:00
4. Sentiment Analysis with VADER (KPIs, negation, emojis, emphasis)
1:28:07
5. Text Classification with Naive Bayes (Multinomial & Bernoulli)
1:45:14
6. Topic Modeling: NMF, LDA, BERTopic & Top2Vec
2:07:55
7. Capstone: End-to-End Travel NLP Pipeline (analyze_message())
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