What you’ll learn
- Design and deploy a complete sentiment analysis pipeline for analyzing customer reviews, combining rule-based and machine learning approaches
- Master text preprocessing techniques and feature extraction methods including TF-IDF, Word Embeddings, and implement custom text classification systems
- Develop production-ready Named Entity Recognition systems using probabilistic approaches and integrate them with modern NLP libraries like spaCy
- Create and train sophisticated language models using Bayesian methods, including Naive Bayes classifiers and Bayesian Networks for text analysis
- Build a comprehensive e-commerce review analysis system that combines sentiment analysis, entity recognition, and topic modeling in a real-world application
- Build and implement probability-based Natural Language Processing models from scratch using Python, including N-grams, Hidden Markov Models, and PCFGs
How to Enroll NLP in Python: Probability Models, Statistics, Text Analysis course?
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