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This course is part of Natural Language Processing Specialization
Instructors: Younes Bensouda Mourri
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85,630 already enrolled
(1,736 reviews)
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Intermediate level
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & stats
(1,736 reviews)
Recommended experience
Intermediate level
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & stats
Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.
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In Course 2 of the Natural Language Processing Specialization, you will:
a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!
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!
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!
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.
22 videos23 readings1 assignment1 programming assignment5 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Reviewed on Sep 10, 2020
This is one of the best courses i have taken. I have learned a lot from this course. Assignments were great and challenging. Thank you deeplearning.ai team for this amazing course.
Reviewed on Feb 12, 2021
Nicely broken into digestible chunks. Labs well done, not too easy, and too too frustrating. Material presented clearly and in (again) nice small steps.
Reviewed on Sep 20, 2024
I felt like I learned some new things from this course. Some of the maths was not as rigorous as it might have been. For example, the proof for Levenstein wasn't complete.
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