DeepLearning.AI - Natural Language Processing with Classification and Vector Spaces
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Natural Language Processing with Classification and Vector Spaces at Coursera Overview
Duration | 29 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Natural Language Processing with Classification and Vector Spaces at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Natural Language Processing with Classification and Vector Spaces at Coursera Course details
- In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
- a) Perform sentiment analysis of tweets using logistic regression and then na''¯ve 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.
- Please make sure that you're comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability.
- 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. '?ukasz 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 with Classification and Vector Spaces at Coursera Curriculum
Sentiment Analysis with Logistic Regression
Welcome to the NLP Specialization
Welcome to Course 1
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
Andrew Ng with Chris Manning
Connect with your mentors and fellow learners on Slack!
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
How to refresh your workspace
Sentiment Analysis with Naïve Bayes
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
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
Vector Space Models
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
Machine Translation and Document Search
Overview
Transforming word vectors
K-nearest neighbors
Hash tables and hash functions
Locality sensitive hashing
Multiple Planes
Approximate nearest neighbors
Searching documents
Andrew Ng with Kathleen McKeown
Acknowledgements
Bibliography
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