About this Course

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Flexible deadlines
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Shareable Certificate
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Coursera Labs
Includes hands on learning projects.
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Intermediate Level

Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & stats

Approx. 31 hours to complete
English

What you will learn

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

Skills you will gain

  • Word2vec
  • Parts-of-Speech Tagging
  • N-gram Language Models
  • Autocorrect
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs External Link
Intermediate Level

Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & stats

Approx. 31 hours to complete
English

Offered by

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DeepLearning.AI

Syllabus - What you will learn from this course

Content RatingThumbs Up91%(7,711 ratings)
Week1
Week 1
7 hours to complete

Autocorrect

7 hours to complete
11 videos (Total 31 min), 10 readings, 4 quizzes
Week2
Week 2
6 hours to complete

Part of Speech Tagging and Hidden Markov Models

6 hours to complete
13 videos (Total 43 min), 11 readings, 3 quizzes
Week3
Week 3
9 hours to complete

Autocomplete and Language Models

9 hours to complete
11 videos (Total 54 min), 9 readings, 3 quizzes
Week4
Week 4
9 hours to complete

Word embeddings with neural networks

9 hours to complete
22 videos (Total 73 min), 21 readings, 3 quizzes

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About the Natural Language Processing Specialization

Natural Language Processing

Frequently Asked Questions

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