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Natural Language Processing with Classification and Vector Spaces

Younes Bensouda Mourri, Łukasz Kaiser, and Eddy Shyu

4.6

Based on 4,495 ratings

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Younes Bensouda Mourri, Łukasz Kaiser, and Eddy Shyu

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.

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.

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What's inside

Syllabus

Sentiment Analysis with Logistic Regression

Learn to extract features from text into numerical vectors, then build a binary classifier for tweets using a logistic regression!

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Read about what's good

what should give you pause

and possible dealbreakers

Explores sentiment analysis, machine translation, and document search, which are core skills for data scientists and linguists

Taught by experts in NLP, machine learning, and deep learning at Stanford University and Google Brain, who are recognized for their work in the field

Develops skills in logistic regression, naive Bayes, vector space models, locality sensitive hashing, and machine translation, which are foundational to NLP

Uses real-world examples and a mix of theory and practice to solidify understanding

Part of a Specialization that covers question-answering, sentiment analysis, machine translation, text summarization, and chatbot development, providing a comprehensive overview of NLP

May be less suitable for complete beginners in data science or NLP, as it assumes some familiarity with these fields

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Reviews summary

Foundational nlp with practical applications

According to students, this course provides a strong theoretical foundation and practical coding experience in Natural Language Processing. Learners often highlight the clarity of explanations for complex topics like logistic regression, Naïve Bayes, and vector space models. The programming assignments are frequently praised for being hands-on and highly relevant to real-world applications such as sentiment analysis and machine translation. Many appreciate the expertise of the instructors, though some mention that a solid background in linear algebra and Python is essential to keep up with the challenging content. Overall, it's considered an excellent introduction to the broader NLP specialization.

Requires a strong foundation in math and programming.

"I strongly recommend having a solid understanding of linear algebra and calculus before starting this course."

"Learners should be comfortable with Python and NumPy, as the coding sections move fairly quickly."

"While the course provides good explanations, I found myself reviewing my math fundamentals to fully keep up."

Taught by highly knowledgeable and engaging experts.

"The instructors' expertise is evident throughout the course; their insights made learning much more engaging."

"I found Younes Bensouda Mourri and Łukasz Kaiser to be excellent teachers, simplifying intricate topics."

"It's clear that the instructors are leaders in the field, which adds immense credibility to the content."

Builds a robust base for advanced NLP studies.

"This course provided me with an excellent groundwork for understanding more advanced NLP topics in the specialization."

"I gained a solid understanding of core NLP techniques, which felt like a great stepping stone."

"It's a perfect starting point if you want to seriously dive into Natural Language Processing."

Assignments provide valuable hands-on coding experience.

"The programming assignments are the strongest part of the course; they helped solidify my understanding through application."

"I loved the practical coding exercises; they were challenging but incredibly rewarding for applying what I learned."

"Working on the tweet classification and translation problems gave me real confidence in implementing NLP models."

Complex NLP concepts are explained with notable clarity.

"The instructors did an amazing job explaining difficult concepts like vector spaces and PCA in an understandable way."

"I really appreciated how the course breaks down complex algorithms into bite-sized, digestible lessons."

"The lectures were very clear, helping me grasp the theory behind sentiment analysis and machine translation much faster."

Pacing can be fast for beginners, requiring extra effort.

"Sometimes the course felt a bit rushed, especially in the later modules on LSH; I had to pause and re-watch."

"The pace was quite fast for a foundational course, which made it challenging for someone completely new to NLP."

"I needed to supplement the lectures with external resources to fully grasp some of the more advanced concepts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Natural Language Processing with Classification and Vector Spaces with these activities:

Vector space models tutorial

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Supplement your knowledge of vector space models through interactive tutorials.

Browse courses on Vector Space Models

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Follow an online tutorial on vector space models.
Implement a simple vector space model using Python or R.

PCA for dimensionality reduction

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Reinforce your grasp of PCA and its role in reducing the dimensionality of vector spaces.

Browse courses on PCA

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Implement PCA in Python or R.
Use PCA to reduce the dimensionality of a vector space related to the course topics.
Visualize the results of dimensionality reduction.

K-nearest neighbors tutorial

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Delve deeper into k-nearest neighbors with hands-on tutorials.

Browse courses on K-Nearest Neighbors

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Complete an online tutorial on k-nearest neighbors.
Implement k-nearest neighbors in Python or R.

Four other activities

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Show all seven activities

Practice sentiment analysis with logistic regression

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Enhance your understanding of logistic regression and its application in sentiment analysis.

Browse courses on Logistic Regression

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Implement logistic regression from scratch.
Use logistic regression to classify tweets as positive or negative.
Evaluate the performance of your classifier.

Practice sentiment analysis with Naive Bayes

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Gain practical experience in leveraging Naive Bayes for sentiment analysis.

Browse courses on Naive Bayes

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Implement Naive Bayes from scratch.
Use Naive Bayes to classify tweets as positive or negative.
Evaluate the performance of your classifier.

Machine translation using word embeddings

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Demonstrate your understanding of machine translation by building a simple system.

Browse courses on Machine Translation

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Use pre-computed word embeddings to create vector representations of words.
Implement locality-sensitive hashing to perform approximate k-nearest neighbor search.
Create a simple machine translation algorithm using the above techniques.

English to French translation app

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Showcase your mastery of natural language processing by developing an English to French translation tool.

Browse courses on Machine Translation

Show steps

Gather a dataset of English and French sentences.
Preprocess and tokenize the sentences.
Create word embeddings for both English and French words.
Implement a machine translation algorithm using the above techniques.
Create a simple user interface for the translation app.

Career center

Learners who complete Natural Language Processing with Classification and Vector Spaces will develop knowledge and skills that may be useful to these careers:

Technical Writer

Technical Writers convey complex technical information to a specific audience in a clear and concise way. They use a variety of writing styles and formats to create documents such as user manuals, white papers, training materials, and marketing collateral. This course provides a foundation in natural language processing (NLP), which is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP is used in a variety of applications, including machine translation, spam filtering, and sentiment analysis. The skills you learn in this course will help you to understand the fundamental concepts of NLP and to apply them to your work.

See salaries and explore the career path for Technical Writer

Web Developer

Web Developers build and maintain websites by writing code that controls the appearance and functionality of the site. They use a variety of programming languages and technologies to create websites that are user-friendly, efficient, and accessible. This course provides a foundation in NLP, which can be used to improve the functionality of websites. For example, NLP can be used to create search engines that can understand natural language queries, or to create chatbots that can interact with users in a natural way. The skills you learn in this course will help you to build websites that are more user-friendly and engaging.

See salaries and explore the career path for Web Developer

Software Engineer

Software Engineers design, develop, and maintain software systems. They use a variety of programming languages and tools to create software that meets the needs of users. This course provides a foundation in NLP, which can be used to improve the functionality of software systems. For example, NLP can be used to create natural language interfaces for software, or to develop software that can understand and respond to natural language commands. The skills you learn in this course will help you to develop software that is more user-friendly and efficient.

See salaries and explore the career path for Software Engineer

Data Scientist

Data Scientists use data to solve problems and make predictions. They use a variety of statistical and machine learning techniques to analyze data and extract insights. This course provides a foundation in NLP, which can be used to improve the accuracy of data analysis. For example, NLP can be used to identify and extract key information from text data, or to develop models that can predict future events. The skills you learn in this course will help you to become a more effective Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models. They use a variety of techniques to train models that can learn from data and make predictions. This course provides a foundation in NLP, which can be used to improve the accuracy of machine learning models. For example, NLP can be used to create natural language interfaces for machine learning models, or to develop models that can understand and respond to natural language commands. The skills you learn in this course will help you to become a more effective Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data and analysis to help businesses make better decisions. They use a variety of techniques to collect, analyze, and interpret data, and to develop recommendations for businesses. This course provides a foundation in NLP, which can be used to improve the accuracy of business analysis. For example, NLP can be used to analyze customer feedback, or to develop models that can predict future business trends. The skills you learn in this course will help you to become a more effective Business Analyst.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians use data to make inferences about the world. They use a variety of statistical techniques to analyze data and draw conclusions. This course provides a foundation in NLP, which can be used to improve the accuracy of statistical analysis. For example, NLP can be used to identify and extract key information from text data, or to develop models that can predict future events. The skills you learn in this course will help you to become a more effective Statistician.

See salaries and explore the career path for Statistician

Quantitative Researcher

Quantitative Researchers use data to make investment decisions. They use a variety of statistical and machine learning techniques to analyze data and identify investment opportunities. This course provides a foundation in NLP, which can be used to improve the accuracy of investment research. For example, NLP can be used to analyze company filings, or to develop models that can predict future stock prices. The skills you learn in this course will help you to become a more effective Quantitative Researcher.

See salaries and explore the career path for Quantitative Researcher

Product Manager

Product Managers are responsible for the development and marketing of products. They work with a variety of stakeholders to define product requirements, develop product specifications, and launch products to market. This course provides a foundation in NLP, which can be used to improve the product development process. For example, NLP can be used to analyze customer feedback, or to develop models that can predict future product demand. The skills you learn in this course will help you to become a more effective Product Manager.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers are responsible for the planning and execution of marketing campaigns. They work with a variety of stakeholders to develop marketing plans, create marketing materials, and track marketing results. This course provides a foundation in NLP, which can be used to improve the effectiveness of marketing campaigns. For example, NLP can be used to analyze customer feedback, or to develop models that can predict future marketing trends. The skills you learn in this course will help you to become a more effective Marketing Manager.

See salaries and explore the career path for Marketing Manager

Content Writer

Content Writers create written content for a variety of purposes, such as marketing, advertising, and public relations. They use a variety of writing styles and formats to create content that is engaging and informative. This course provides a foundation in NLP, which can be used to improve the quality of written content. For example, NLP can be used to identify and extract key information from text data, or to develop models that can generate natural language text. The skills you learn in this course will help you to become a more effective Content Writer.

See salaries and explore the career path for Content Writer

Editor

Editors review and edit written content for a variety of purposes, such as grammar, spelling, and style. They work with a variety of stakeholders to ensure that written content is clear, concise, and accurate. This course provides a foundation in NLP, which can be used to improve the efficiency and effectiveness of editing. For example, NLP can be used to identify and correct grammatical errors, or to develop models that can automatically check for plagiarism. The skills you learn in this course will help you to become a more effective Editor.

See salaries and explore the career path for Editor

Translator

Translators convert written content from one language to another. They use a variety of techniques to ensure that the translated content is accurate and culturally appropriate. This course provides a foundation in NLP, which can be used to improve the quality of translations. For example, NLP can be used to develop machine translation systems that can automatically translate text from one language to another. The skills you learn in this course will help you to become a more effective Translator.

See salaries and explore the career path for Translator

Customer Service Representative

Customer Service Representatives provide support to customers by answering questions, resolving complaints, and providing information. They work with a variety of customers to ensure that they have a positive experience with the company. This course provides a foundation in NLP, which can be used to improve the efficiency and effectiveness of customer service. For example, NLP can be used to develop chatbots that can automatically answer customer questions, or to analyze customer feedback to identify areas for improvement. The skills you learn in this course will help you to become a more effective Customer Service Representative.

See salaries and explore the career path for Customer Service Representative

Salesperson

Salespeople sell products or services to customers. They work with a variety of customers to identify their needs and help them find the best product or service for their needs. This course provides a foundation in NLP, which can be used to improve the effectiveness of sales. For example, NLP can be used to analyze customer data to identify potential sales leads, or to develop models that can predict customer churn. The skills you learn in this course will help you to become a more effective Salesperson.

See salaries and explore the career path for Salesperson

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Natural Language Processing with Classification and Vector Spaces.

Cyberformalism

Save

While more comprehensive, it classic in the field and provides a solid foundation in NLP.

Cyberformalism: Histories of Linguistic Forms in...

Hardcover

Cyberformalism: Histories of Linguistic Forms in...

Kindle Edition

The Elements of Statistical Learning

Save

Provides a comprehensive overview of statistical learning methods.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Deep Learning

Save

Provides a comprehensive overview of deep learning.

Deep Learning

Paperback

$$$

Deep Learning (Adaptive Computation and Machine...

Hardcover

$$$

Deep Learning (Adaptive Computation and Machine...

Kindle Edition

$$$

Natural Language Processing with Python

Save

It good introduction to the field of NLP, covering a wide range of topics.

Natural Language Processing with Python: Analyzing...

Paperback

$$$

(中文) Python Natural Language Processing(Chinese Edition)

Paperback

Natural Language Processing with Python: Analyzing...

Kindle Edition

Natural Language Processing in Action

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Provides a practical introduction to NLP with Python.

Natural Language Processing in Action:...

Paperback

Natural Language Processing in Action:...

Kindle Edition

Text Mining with R

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Provides a comprehensive overview of text mining techniques in R.

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Rating

4.6

Effort

Approx. 33 hours

Level

Intermediate

Via

Coursera

Institution

DeepLearning.AI

Instructors

Younes Bensouda Mourri

Łukasz Kaiser

Eddy Shyu

Language

English

Transcripts

العربية Français বাংলা українська мова 中文 Ελληνικά Italiano Português Nederlands 한국어 Deutsch پښتو اُردُو, Pусский ไทย Bahasa Indonesia Svenska Türkçe آذربایجان دیلی Español हिंदी 日本語 қазақша فارسی magyar nyelv Polski

This course belongs to a collection called Natural Language Processing. It's comprised of four courses:

Natural Language Processing with Attention Models
Natural Language Processing with Classification and Vector Spaces (viewing)
Natural Language Processing with Probabilistic Models
Natural Language Processing with Sequence Models

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores sentiment analysis, machine translation, and document search, which are core skills for data scientists and linguists

Taught by experts in NLP, machine learning, and deep learning at Stanford University and Google Brain, who are recognized for their work in the field

Develops skills in logistic regression, naive Bayes, vector space models, locality sensitive hashing, and machine translation, which are foundational to NLP

Uses real-world examples and a mix of theory and practice to solidify understanding

Part of a Specialization that covers question-answering, sentiment analysis, machine translation, text summarization, and chatbot development, providing a comprehensive overview of NLP

May be less suitable for complete beginners in data science or NLP, as it assumes some familiarity with these fields

Natural Language Processing with Classification and Vector Spaces

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Foundational nlp with practical applications

Activities

Career center

Reading list

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