About this Course

484,939 recent views
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English
Subtitles: English, Japanese

Skills you will gain

Machine TranslationWord EmbeddingsLocality-Sensitive HashingSentiment AnalysisVector Space Models
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English
Subtitles: English, Japanese

Offered by

Placeholder

deeplearning.ai

Syllabus - What you will learn from this course

Content RatingThumbs Up96%(5,717 ratings)Info
Week
1

Week 1

7 hours to complete

Sentiment Analysis with Logistic Regression

7 hours to complete
12 videos (Total 37 min), 3 readings, 1 quiz
12 videos
Welcome to Course 11m
Supervised ML & Sentiment Analysis2m
Vocabulary & Feature Extraction2m
Negative and Positive Frequencies2m
Feature Extraction with Frequencies2m
Preprocessing3m
Putting it All Together2m
Logistic Regression Overview3m
Logistic Regression: Training1m
Logistic Regression: Testing4m
Logistic Regression: Cost Function5m
3 readings
Connect with your mentors and fellow learners on Slack!10m
Acknowledgement - Ken Church10m
How to refresh your workspace10m
Week
2

Week 2

5 hours to complete

Sentiment Analysis with Naïve Bayes

5 hours to complete
11 videos (Total 40 min)
11 videos
Bayes’ Rule3m
Naïve Bayes Introduction5m
Laplacian Smoothing2m
Log Likelihood, Part 15m
Log Likelihood, Part 21m
Training Naïve Bayes3m
Testing Naïve Bayes4m
Applications of Naïve Bayes3m
Naïve Bayes Assumptions3m
Error Analysis3m
Week
3

Week 3

6 hours to complete

Vector Space Models

6 hours to complete
8 videos (Total 26 min)
8 videos
Word by Word and Word by Doc. 4m
Euclidean Distance3m
Cosine Similarity: Intuition2m
Cosine Similarity3m
Manipulating Words in Vector Spaces3m
Visualization and PCA3m
PCA Algorithm3m
Week
4

Week 4

6 hours to complete

Machine Translation and Document Search

6 hours to complete
8 videos (Total 29 min), 2 readings, 1 quiz
8 videos
Transforming word vectors6m
K-nearest neighbors3m
Hash tables and hash functions3m
Locality sensitive hashing5m
Multiple Planes3m
Approximate nearest neighbors3m
Searching documents1m
2 readings
Acknowledgements10m
Bibliography10m

Reviews

TOP REVIEWS FROM NATURAL LANGUAGE PROCESSING WITH CLASSIFICATION AND VECTOR SPACES

View all reviews

About the Natural Language Processing Specialization

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. 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....
Natural Language Processing

Frequently Asked Questions

More questions? Visit the Learner Help Center.