Master Language Models, Hidden Markov Models, Bayesian Methods & Sentiment Analysis for Real-World Applications
What you will learn
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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
