What will you learn in this Natural Language Processing with Probabilistic Models Course
Implement autocorrect algorithms using minimum edit distance and dynamic programming.
Apply Hidden Markov Models and the Viterbi algorithm for part-of-speech tagging.
Develop N-gram language models for autocomplete functionalities.
Build Word2Vec models to generate word embeddings using neural networks.
Program Overview
1. Autocorrect
⏳ 6 hours
Learn about autocorrect mechanisms, focusing on minimum edit distance and dynamic programming to correct misspelled words.
2. Part of Speech Tagging and Hidden Markov Models
⏳ 5 hours
Understand Markov chains and Hidden Markov Models, and apply the Viterbi algorithm for tagging parts of speech in text corpora
3. Autocomplete and Language Models
⏳ 8 hours
Explore N-gram language models to calculate sequence probabilities and build autocomplete systems using textual data.
4. Word Embeddings with Neural Networks
⏳ 9 hours
Delve into word embeddings, learning to create Continuous Bag-of-Words (CBOW) models to capture semantic meanings of words.
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Job Outlook
Implement autocorrect algorithms using minimum edit distance and dynamic programming.
Apply Hidden Markov Models and the Viterbi algorithm for part-of-speech tagging.
Develop N-gram language models for autocomplete functionalities.
Build Word2Vec models to generate word embeddings using neural networks.
Specification: Natural Language Processing with Probabilistic Models
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