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Natural Language Processing with Classification and Vector Spaces

Approx. 31 hours to complete

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Course Summary

This course teaches the fundamental concepts of natural language processing (NLP) and classification using vector spaces. You'll learn how to represent words as vectors, build classifiers, and apply them to various NLP tasks.

Key Learning Points

Learn the basics of natural language processing and vector spaces
Understand how to represent words as vectors and build classifiers
Apply classification techniques to various NLP tasks

Job Positions & Salaries of people who have taken this course might have

- USA: $110,000 - $150,000
- India: ₹8,00,000 - ₹20,00,000
- Spain: €30,000 - €50,000
- USA: $110,000 - $150,000
- India: ₹8,00,000 - ₹20,00,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $140,000
- India: ₹6,00,000 - ₹16,00,000
- Spain: €25,000 - €45,000
- USA: $110,000 - $150,000
- India: ₹8,00,000 - ₹20,00,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $140,000
- India: ₹6,00,000 - ₹16,00,000
- Spain: €25,000 - €45,000
- USA: $100,000 - $150,000
- India: ₹7,00,000 - ₹20,00,000
- Spain: €25,000 - €50,000

Learning Outcomes

Ability to apply classification techniques to various NLP tasks
Understanding of how to represent words as vectors
Knowledge of fundamental concepts of NLP and vector spaces

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with linear algebra and calculus

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Applied Text Mining in Python
Natural Language Processing with Probabilistic Models
Deep Learning for Natural Language Processing

Related Education Paths

Natural Language Processing Specialization

Notable People in This Field

Research Scientist at DeepMind
Professor of Linguistics at University of Washington

Related Books

Description

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 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. 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. 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.

Outline

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
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

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

Summary of User Reviews

Learn about classification vector spaces in NLP with this highly rated course on Coursera. Students appreciate the practical applications of the material and the engaging instructor.

Pros from User Reviews

Engaging instructor who teaches complex concepts in an easy-to-understand manner
Real-world examples and assignments help students apply what they've learned
Great introduction to NLP and machine learning
Good balance between theory and practice

Cons from User Reviews

Some students found the material too basic
The course can be challenging for beginners
The pace may be too slow for advanced learners

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