Bayesian Parameter Estimation plays a key role in Natural Language Processing (NLP) by helping models learn from data using probability and prior knowledge.
It combines Bayes’ Theorem with statistical learning to estimate parameters in language models like Naive Bayes, HMMs, and topic models.
In this video, you’ll learn:
What is Bayesian Parameter Estimation?
How it’s applied in NLP and Machine Learning
Concepts of prior, likelihood, and posterior
Real-world examples in text classification, speech recognition, and language modeling
Understand how Bayesian methods make NLP models smarter, more adaptive, and data-efficient!
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