NLP in Python: Probability Models, Statistics, Text Analysis

NLP in Python: Probability Models, Statistics, Text Analysis

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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