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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ogk84p/natural
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!
Read more
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!
Read more
Sentiment Analysis with Naïve Bayes
Learn the theory behind Bayes' rule for conditional probabilities, then apply it toward building a Naive Bayes tweet classifier of your own!
Vector Space Models
Vector space models capture semantic meaning and relationships between words. You'll learn how to create word vectors that capture dependencies between words, then visualize their relationships in two dimensions using PCA.
Machine Translation and Document Search
Learn to transform word vectors and assign them to subsets using locality sensitive hashing, in order to perform machine translation and document search.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Intro to nlp with classification & vector spaces
This course introduces the foundational concepts of Natural Language Processing (NLP) with classification and vector space models. Learners will explore how to represent text as vectors, leveraging these representations for tasks like sentiment analysis and machine translation. The course emphasizes hands-on learning through interactive assignments and projects, empowering learners to apply these techniques in real-world NLP applications.
Guided by experienced instructors, learners will navigate weekly topics including sentiment analysis using logistic regression and Naive Bayes, dimensionality reduction with Principal Component Analysis, and word embeddings for representing words in vector space. They will also delve into locality-sensitive hashing for efficient nearest neighbor search and gain practical experience in implementing various NLP algorithms from scratch.
Throughout the course, learners will benefit from numerous resources to support their understanding, including video lectures, readings, quizzes, and discussion forums. By the end of the course, they will have developed a solid foundation in NLP principles and be equipped with the skills to tackle more advanced topics in the field.
This course includes interactive and hands-on assignments that allow learners to apply the concepts they learn. These assignments provide valuable practice and help reinforce understanding, ensuring that learners can implement NLP techniques effectively.
"Throughout the course, learners will benefit from numerous resources to support their understanding, including video lectures, readings, quizzes, and discussion forums."
"By the end of the course, they will have developed a solid foundation in NLP principles and be equipped with the skills to tackle more advanced topics in the field."
The course is designed to be accessible to learners with little to no prior knowledge in NLP. It gradually introduces the concepts, starting with the basics and building upon them, making it suitable for beginners who want to establish a solid foundation in the field.
"This course introduces the foundational concepts of Natural Language Processing (NLP) with classification and vector space models."
The course offers a well-rounded curriculum covering foundational concepts in NLP. Learners explore various topics such as sentiment analysis, dimensionality reduction, word embeddings, and locality-sensitive hashing, providing a comprehensive understanding of the field.
"This course introduces the foundational concepts of Natural Language Processing (NLP) with classification and vector space models."
"Guided by experienced instructors, learners will navigate weekly topics including sentiment analysis using logistic regression and Naive Bayes, dimensionality reduction with Principal Component Analysis, and word embeddings for representing words in vector space."
"They will also delve into locality-sensitive hashing for efficient nearest neighbor search"
The course is guided by experienced instructors who provide clear explanations and insights throughout the learning journey. Learners benefit from well-structured video lectures, readings, and quizzes, ensuring a comprehensive understanding of the concepts.
"Guided by experienced instructors, learners will navigate weekly topics including sentiment analysis using logistic regression and Naive Bayes, dimensionality reduction with Principal Component Analysis, and word embeddings for representing words in vector space."
This course provides learners with hands-on experience in implementing various NLP algorithms from scratch. Through interactive assignments and projects, they develop practical skills in tasks like sentiment analysis, dimensionality reduction, and word embedding, preparing them to apply these techniques in real-world NLP applications.
"The course emphasizes hands-on learning through interactive assignments and projects, empowering learners to apply these techniques in real-world NLP applications."
"They will also delve into locality-sensitive hashing for efficient nearest neighbor search and gain practical experience in implementing various NLP algorithms from scratch."
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
Show steps
Supplement your knowledge of vector space models through interactive tutorials.
Browse courses on
Vector Space Models
Show steps
-
Follow an online tutorial on vector space models.
-
Implement a simple vector space model using Python or R.
PCA for dimensionality reduction
Show steps
Reinforce your grasp of PCA and its role in reducing the dimensionality of vector spaces.
Browse courses on
PCA
Show steps
-
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
Show steps
Delve deeper into k-nearest neighbors with hands-on tutorials.
Browse courses on
K-Nearest Neighbors
Show steps
-
Complete an online tutorial on k-nearest neighbors.
-
Implement k-nearest neighbors in Python or R.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice sentiment analysis with logistic regression
Show steps
Enhance your understanding of logistic regression and its application in sentiment analysis.
Browse courses on
Logistic Regression
Show steps
-
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
Show steps
Gain practical experience in leveraging Naive Bayes for sentiment analysis.
Browse courses on
Naive Bayes
Show steps
-
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
Show steps
Demonstrate your understanding of machine translation by building a simple system.
Browse courses on
Machine Translation
Show steps
-
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
Show steps
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.
Vector space models tutorial
Show steps
Supplement your knowledge of vector space models through interactive tutorials.
Browse courses on
Vector Space Models
Show steps
Show steps
Show steps
- Follow an online tutorial on vector space models.
- Implement a simple vector space model using Python or R.
PCA for dimensionality reduction
Show steps
Reinforce your grasp of PCA and its role in reducing the dimensionality of vector spaces.
Browse courses on
PCA
Show steps
Show steps
Show steps
- 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
Show steps
Delve deeper into k-nearest neighbors with hands-on tutorials.
Browse courses on
K-Nearest Neighbors
Show steps
Show steps
Show steps
- Complete an online tutorial on k-nearest neighbors.
- Implement k-nearest neighbors in Python or R.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice sentiment analysis with logistic regression
Show steps
Enhance your understanding of logistic regression and its application in sentiment analysis.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- 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
Show steps
Gain practical experience in leveraging Naive Bayes for sentiment analysis.
Browse courses on
Naive Bayes
Show steps
Show steps
Show steps
- 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
Show steps
Demonstrate your understanding of machine translation by building a simple system.
Browse courses on
Machine Translation
Show steps
Show steps
Show steps
- 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
Show steps
Showcase your mastery of natural language processing by developing an English to French translation tool.
Browse courses on
Machine Translation
Show steps
Show steps
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 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.
Web Developer
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.
Quantitative Researcher
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.
Business Analyst
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.
Statistician
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.
Software Engineer
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.
Data Scientist
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.
Machine Learning Engineer
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.
Content Writer
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.
Product Manager
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.
Editor
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.
Translator
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.
Marketing 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.
Salesperson
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.
Customer Service Representative
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.
For more career information including salaries, visit:
OpenCourser.com/course/ogk84p/natural
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.
Save
While more comprehensive, it classic in the field and provides a solid foundation in NLP.
Provides a comprehensive overview of statistical learning methods.
Save
Provides a comprehensive overview of deep learning.
It good introduction to the field of NLP, covering a wide range of topics.
Provides a practical introduction to NLP with Python.
Save
Provides a comprehensive overview of text mining techniques in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ogk84p/natural
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete one Course.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Younes Bensouda Mourri
Łukasz Kaiser
Eddy Shyu
Language
English
Transcripts
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Português
Deutsch
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中文
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آذربایجان دیلی
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
Good to know
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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