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Natural Language Processing with Classification and Vector Spaces
  • Level Professional
  • Duration 33 hours
  • Course by DeepLearning.AI
  • Offered by

About

In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search. 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, and even built a chatbot! 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.

Modules

Lecture: Logistic Regression

    • 14 Videos
    • 12 Readings
    • 1 ExternalTool
    • 3 Lab
Show info about module content

1 External Tool

  • Intake Survey

3 Labs

  • Natural Language preprocessing
  • Visualizing word frequencies
  • Visualizing tweets and Logistic Regression models

14 Videos

  • Welcome to the NLP Specialization
  • Welcome to Course 1
  • Week Introduction
  • Supervised ML & Sentiment Analysis
  • Vocabulary & Feature Extraction
  • Negative and Positive Frequencies
  • Feature Extraction with Frequencies
  • Preprocessing
  • Putting it All Together
  • Logistic Regression Overview
  • Logistic Regression: Training
  • Logistic Regression: Testing
  • Logistic Regression: Cost Function
  • Week Conclusion

12 Readings

  • Acknowledgement - Ken Church
  • Supervised ML & Sentiment Analysis
  • Vocabulary & Feature Extraction
  • Feature Extraction with Frequencies
  • Preprocessing
  • Putting it all together
  • Logistic Regression Overview
  • Logistic Regression: Training
  • Logistic Regression: Testing
  • Optional Logistic Regression: Cost Function
  • Optional Logistic Regression: Gradient
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W1

Practice Quiz

    • 1 Assignment
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1 Assignment

  • Logistic Regression

Assignment: Sentiment Analysis with Logistic Regression

    • 1 Readings
    • 1 Programming
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1 Programming

  • Logistic Regression

1 Readings

  • (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace

Heroes of NLP: Chris Manning (Optional)

    • 1 Videos
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1 Videos

  • Andrew Ng with Chris Manning

Lecture: Naive Bayes

    • 13 Videos
    • 11 Readings
    • 1 Lab
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1 Labs

  • Visualizing likelihoods and confidence ellipses

13 Videos

  • Week Introduction
  • Probability and Bayes’ Rule
  • Bayes’ Rule
  • Naïve Bayes Introduction
  • Laplacian Smoothing
  • Log Likelihood, Part 1
  • Log Likelihood, Part 2
  • Training Naïve Bayes
  • Testing Naïve Bayes
  • Applications of Naïve Bayes
  • Naïve Bayes Assumptions
  • Error Analysis
  • Week Conclusion

11 Readings

  • Probability and Bayes’ Rule
  • Bayes' Rule
  • Naive Bayes Introduction
  • Laplacian Smoothing
  • Log Likelihood, Part 1
  • Log Likelihood Part 2
  • Training naïve Bayes
  • Testing naïve Bayes
  • Applications of Naive Bayes
  • Naïve Bayes Assumptions
  • Error Analysis

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W2

Practice Quiz

    • 1 Assignment
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1 Assignment

  • Naive Bayes

Assignment: Naive Bayes

    • 1 Programming
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1 Programming

  • Naive Bayes

Lecture: Vector Space Models

    • 10 Videos
    • 9 Readings
    • 3 Lab
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3 Labs

  • Linear algebra in Python with Numpy
  • Manipulating word embeddings
  • Another explanation about PCA

10 Videos

  • Week Introduction
  • Vector Space Models
  • Word by Word and Word by Doc.
  • Euclidean Distance
  • Cosine Similarity: Intuition
  • Cosine Similarity
  • Manipulating Words in Vector Spaces
  • Visualization and PCA
  • PCA Algorithm
  • Week Conclusion

9 Readings

  • Vector Space Models
  • Word by Word and Word by Doc.
  • Euclidian Distance
  • Cosine Similarity: Intuition
  • Cosine Similarity
  • Manipulating Words in Vector Spaces
  • Visualization and PCA
  • PCA algorithm
  • The Rotation Matrix (Optional Reading)

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W3

Practice Quiz

    • 1 Assignment
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1 Assignment

  • Vector Space Models

Assignment: Vector Space Models

    • 1 Programming
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1 Programming

  • Assignment: Vector Space Models

Lecture: Machine Translation

    • 10 Videos
    • 7 Readings
    • 2 Lab
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2 Labs

  • Rotation matrices in R2
  • Hash tables

10 Videos

  • Week Introduction
  • Overview
  • Transforming word vectors
  • K-nearest neighbors
  • Hash tables and hash functions
  • Locality sensitive hashing
  • Multiple Planes
  • Approximate nearest neighbors
  • Searching documents
  • Week Conclusion

7 Readings

  • Transforming word vectors
  • K-nearest neighbors
  • Hash tables and hash functions
  • Locality sensitive hashing
  • Multiple Planes
  • Approximate nearest neighbors
  • Searching documents

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes W4

Practice Quiz

    • 1 Assignment
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1 Assignment

  • Hashing and Machine Translation

End of access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Assignment: Machine Translation

    • 1 Programming
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1 Programming

  • Word Translation

Acknowledgments and Bibliography

    • 2 Readings
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2 Readings

  • Acknowledgements
  • Bibliography

Heroes of NLP: Kathleen McKeown

    • 1 Videos
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1 Videos

  • Andrew Ng with Kathleen McKeown

Auto Summary

Discover the world of Natural Language Processing in this comprehensive course focused on classification and vector spaces within Data Science & AI. Led by Stanford's Younes Bensouda Mourri and Google's Łukasz Kaiser, you'll master sentiment analysis, vector space models, and translation algorithms. Perfect for professionals, the course spans 1980 minutes and offers various subscription options to suit your learning needs. Join now to enhance your NLP skills and build practical applications in question-answering, language translation, and text summarization.

Younes Bensouda Mourri
Instructors

Younes Bensouda Mourri

Łukasz Kaiser
Instructors

Łukasz Kaiser