DeepLearning.AI
Natural Language Processing with Probabilistic Models
DeepLearning.AI

Natural Language Processing with Probabilistic Models

Younes Bensouda Mourri
Łukasz Kaiser

Instructors: Younes Bensouda Mourri

82,105 already enrolled

Gain insight into a topic and learn the fundamentals.
4.7

(1,708 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 30 hours
Learn at your own pace
92%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

(1,708 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 30 hours
Learn at your own pace
92%
Most learners liked this course

What you'll learn

  • Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

Details to know

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Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Natural Language Processing Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 4 modules in this course

Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!

What's included

11 videos11 readings1 assignment1 programming assignment2 ungraded labs

Learn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus!

What's included

13 videos12 readings1 assignment1 programming assignment2 ungraded labs

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!

What's included

11 videos10 readings1 assignment1 programming assignment3 ungraded labs

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.

What's included

22 videos23 readings1 assignment1 programming assignment5 ungraded labs

Instructors

Instructor ratings
4.7 (380 ratings)
Younes Bensouda Mourri
DeepLearning.AI
5 Courses215,343 learners

Offered by

DeepLearning.AI

Recommended if you're interested in Machine Learning

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4.7

1,708 reviews

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