Grasp Language Fashions, Hidden Markov Fashions, Bayesian Strategies & Sentiment Evaluation for Actual-World Functions
What you’ll study
Design and deploy an entire sentiment evaluation pipeline for analyzing buyer evaluations, combining rule-based and machine studying approaches
Grasp textual content preprocessing methods and have extraction strategies together with TF-IDF, Phrase Embeddings, and implement customized textual content classification programs
Develop production-ready Named Entity Recognition programs utilizing probabilistic approaches and combine them with fashionable NLP libraries like spaCy
Create and practice refined language fashions utilizing Bayesian strategies, together with Naive Bayes classifiers and Bayesian Networks for textual content evaluation
Construct a complete e-commerce evaluate evaluation system that mixes sentiment evaluation, entity recognition, and matter modeling in a real-world software
Construct and implement probability-based Pure Language Processing fashions from scratch utilizing Python, together with N-grams, Hidden Markov Fashions, and PCFGs
English
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