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DeepLearning.AI vía Coursera
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Natural Language Processing with Probabilistic Models

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  • Autocorrect
    • Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!
  • Part of Speech Tagging and Hidden Markov Models
    • Learn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus!
  • Autocomplete and Language Models
    • Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter!
  • Word embeddings with neural networks
    • Learn about how word embeddings carry the semantic meaning of words, which makes them much more powerful for NLP tasks, then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.