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Ari Anastassiou
In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes. You will learn how to use natural language processing techniques to generate word embeddings for these ingredients, using Word2Vec. These word embeddings can be used for recommendations in an online store based on added items in a basket, or to suggest alternative items as replacements when stock is limited. You will build this recommendation/discovery feature in an interactive and aesthetic visualization tool.
Read more
In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes. You will learn how to use natural language processing techniques to generate word embeddings for these ingredients, using Word2Vec. These word embeddings can be used for recommendations in an online store based on added items in a basket, or to suggest alternative items as replacements when stock is limited. You will build this recommendation/discovery feature in an interactive and aesthetic visualization tool.
Read more
In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes. You will learn how to use natural language processing techniques to generate word embeddings for these ingredients, using Word2Vec. These word embeddings can be used for recommendations in an online store based on added items in a basket, or to suggest alternative items as replacements when stock is limited. You will build this recommendation/discovery feature in an interactive and aesthetic visualization tool.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/ntts73/interactive
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What's inside
Syllabus
Interactive Word Embeddings using Word2Vec and Plotly
In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes, and prepare the data for use in a word embedding model. You will learn how Word2Vec works, and how to implement this model using Gensim. You will learn about visualizing the results using a similarity matrix, and then build a network graph using NetworkX on top of this. You will learn how to build an visual tool to explore this data in a manner that is both interactive and aesthetically unmatched, using Plotly. This tool can then be used for interactive recommendations, or similar item discovery, for example, to be used in an online supermarket store recommending additional items to be purchased, or offering effective alternatives when there is no stock of a desired item.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners, using a friendly and accessible approach that makes it perfect for those new to the field
Offers hands-on labs and interactive materials, providing learners with practical experience in applying the techniques they learn
Taught by Ari Anastassiou, an experienced instructor in the field, ensuring the course is up-to-date and relevant to industry needs
Examines a unique perspective on natural language processing, specifically in the context of recipe analysis
Develops skills and knowledge that are highly relevant to the food and grocery industry, including product development and marketing
Requires learners to come in with some basic knowledge of natural language processing and Python, which may be a barrier for some
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Save Interactive Word Embeddings using Word2Vec and Plotly to your list so you can find it easily later:
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Reviews summary
Highly rated word embeddings course
Learners say this course is great, amazing, and gives detailed explanations in its video lectures. The project also includes completed notebooks to concentrate on the content and not on typing skills. However, one learner said the course's video has inaccurate information and that they felt frustrated. Overall, learners found this course to be beneficial.
Notebooks allow for focus on content, not typing.
"The completed notebook are in the resource section of the project, so it is possible to concentrate on the videos (and on annotating the code) and not on honing your typing skills."
Videos give detailed explanations.
"The lecturer gave detailed explanations of each step."
"The project showed how to compute word2vec and then how to visualize the word embeddings."
Video in the course has inaccurate information.
"This course really sucks, I don't want to what the lousey video with inaccurate info and then get fusrtrated but simply get some tips on how to prepare my own project"
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 Interactive Word Embeddings using Word2Vec and Plotly with these
activities:
Review Course Materials
Show steps
Helps learners refresh their memory and strengthen their understanding of concepts covered in previous lessons or modules.
Show steps
-
Go through lecture notes, slides, or video recordings.
-
Reread assigned readings or textbooks.
-
Complete practice questions or exercises.
Review Python
Show steps
Provides an overview of the basic concepts and techniques of Python programming, helping learners refresh their understanding and strengthen their foundation before starting the course.
Browse courses on
Natural Language Processing
Show steps
-
Go through online tutorials or documentation on Python basics.
-
Solve coding exercises to practice Python syntax and data structures.
-
Review concepts such as variables, data types, conditional statements, and loops.
Join a Study Group for NLP Discussions
Show steps
Fosters collaboration and knowledge sharing among learners, providing them with a supportive environment to discuss concepts, ask questions, and reinforce their understanding.
Browse courses on
Natural Language Processing
Show steps
-
Find or create a study group with peers enrolled in the same course.
-
Set regular meeting times and establish a communication channel.
-
Discuss course materials, assignments, and concepts.
-
Share resources, tips, and insights with group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Word Embeddings with Gensim
Show steps
Provides learners with hands-on experience in using the Gensim library to create and explore word embeddings, enhancing their understanding of the topic.
Browse courses on
Word Embeddings
Show steps
-
Follow tutorials on installing Gensim and its dependencies.
-
Load a text dataset and preprocess it for word embedding creation.
-
Use Gensim's Word2Vec model to train word embeddings.
-
Visualize the word embeddings using techniques like PCA or t-SNE.
Solve NLP Coding Exercises
Show steps
Reinforces learners' understanding of NLP concepts and techniques by providing them with a series of coding exercises to solve.
Browse courses on
Natural Language Processing
Show steps
-
Find online platforms or repositories that offer NLP coding exercises.
-
Choose exercises that align with the topics covered in the course.
-
Solve the exercises using appropriate NLP libraries and techniques.
-
Review solutions and identify areas for improvement.
Attend an NLP Workshop or Meetup
Show steps
Provides learners with an opportunity to connect with experts, learn from industry professionals, and stay updated on the latest trends and advancements in NLP.
Browse courses on
Natural Language Processing
Show steps
-
Identify NLP workshops or meetups in their area or online.
-
Register and attend the event.
-
Engage with speakers, ask questions, and participate in discussions.
-
Network with other attendees and learn about potential career opportunities.
Build a Word Embedding-based Recommendation Tool
Show steps
Allows learners to apply their knowledge and skills by creating a practical tool that demonstrates the use of word embeddings in a real-world scenario.
Browse courses on
Natural Language Processing
Show steps
-
Design a simple recommendation system architecture using word embeddings.
-
Develop the recommendation engine using a programming language and relevant libraries.
-
Implement a user interface to interact with the recommendation tool.
-
Test and evaluate the tool's performance on a given dataset.
Contribute to Open-Source NLP Projects
Show steps
Provides learners with an opportunity to engage with the broader NLP community, deepen their understanding, and showcase their skills.
Browse courses on
Natural Language Processing
Show steps
-
Identify open-source NLP projects that align with their interests.
-
Review the project documentation and identify areas where they can contribute.
-
Submit bug reports, feature requests, or code contributions to the project.
-
Engage with the project maintainers and other contributors.
Review Course Materials
Show steps
Helps learners refresh their memory and strengthen their understanding of concepts covered in previous lessons or modules.
Show steps
Show steps
Show steps
- Go through lecture notes, slides, or video recordings.
- Reread assigned readings or textbooks.
- Complete practice questions or exercises.
Review Python
Show steps
Provides an overview of the basic concepts and techniques of Python programming, helping learners refresh their understanding and strengthen their foundation before starting the course.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Go through online tutorials or documentation on Python basics.
- Solve coding exercises to practice Python syntax and data structures.
- Review concepts such as variables, data types, conditional statements, and loops.
Join a Study Group for NLP Discussions
Show steps
Fosters collaboration and knowledge sharing among learners, providing them with a supportive environment to discuss concepts, ask questions, and reinforce their understanding.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Find or create a study group with peers enrolled in the same course.
- Set regular meeting times and establish a communication channel.
- Discuss course materials, assignments, and concepts.
- Share resources, tips, and insights with group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Word Embeddings with Gensim
Show steps
Provides learners with hands-on experience in using the Gensim library to create and explore word embeddings, enhancing their understanding of the topic.
Browse courses on
Word Embeddings
Show steps
Show steps
Show steps
- Follow tutorials on installing Gensim and its dependencies.
- Load a text dataset and preprocess it for word embedding creation.
- Use Gensim's Word2Vec model to train word embeddings.
- Visualize the word embeddings using techniques like PCA or t-SNE.
Solve NLP Coding Exercises
Show steps
Reinforces learners' understanding of NLP concepts and techniques by providing them with a series of coding exercises to solve.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Find online platforms or repositories that offer NLP coding exercises.
- Choose exercises that align with the topics covered in the course.
- Solve the exercises using appropriate NLP libraries and techniques.
- Review solutions and identify areas for improvement.
Attend an NLP Workshop or Meetup
Show steps
Provides learners with an opportunity to connect with experts, learn from industry professionals, and stay updated on the latest trends and advancements in NLP.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify NLP workshops or meetups in their area or online.
- Register and attend the event.
- Engage with speakers, ask questions, and participate in discussions.
- Network with other attendees and learn about potential career opportunities.
Build a Word Embedding-based Recommendation Tool
Show steps
Allows learners to apply their knowledge and skills by creating a practical tool that demonstrates the use of word embeddings in a real-world scenario.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Design a simple recommendation system architecture using word embeddings.
- Develop the recommendation engine using a programming language and relevant libraries.
- Implement a user interface to interact with the recommendation tool.
- Test and evaluate the tool's performance on a given dataset.
Contribute to Open-Source NLP Projects
Show steps
Provides learners with an opportunity to engage with the broader NLP community, deepen their understanding, and showcase their skills.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify open-source NLP projects that align with their interests.
- Review the project documentation and identify areas where they can contribute.
- Submit bug reports, feature requests, or code contributions to the project.
- Engage with the project maintainers and other contributors.
Career center
Learners who complete Interactive Word Embeddings using Word2Vec and Plotly will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist combines the skills of a data analyst, a statistician, and a computer scientist. With the help of Word2Vec and Plotly, you can develop interactive dashboards, visualize data trends, and discover patterns that can be difficult to spot manually. These skills are in high demand across various industries, including finance, healthcare, and retail.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze financial data and make investment decisions. By mastering Word2Vec and Plotly, you will gain the ability to create interactive visualizations that can help you identify trading opportunities, assess risk, and make informed decisions.
Market Researcher
Market Researcher
Market Researchers conduct surveys, analyze data, and provide insights to companies on consumer behavior and market trends. This course will equip you with the skills to create interactive visualizations that can help you present your findings in a clear and engaging manner.
Business Analyst
Business Analyst
Business Analysts help companies improve their performance by analyzing data and identifying areas for improvement. Word2Vec and Plotly can help you build interactive dashboards that can track key performance indicators, identify trends, and make recommendations for improvement.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. By mastering Word2Vec and Plotly, you will gain the skills to create interactive visualizations that can help you communicate your findings effectively.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. Word2Vec and Plotly can help you build interactive visualizations that can be used to debug code, identify performance bottlenecks, and improve user experience.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. Word2Vec and Plotly can help you create interactive visualizations that can be used to monitor model performance, identify bias, and improve accuracy.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. Word2Vec and Plotly can help you create interactive visualizations that can be used to track data quality, identify bottlenecks, and improve performance.
Product Manager
Product Manager
Product Managers define the vision, roadmap, and features of a product. Word2Vec and Plotly can help you create interactive visualizations that can be used to gather user feedback, track product usage, and make data-driven decisions.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces. Word2Vec and Plotly can help you create interactive prototypes that can be used to test user flows, gather feedback, and improve the overall user experience.
Financial Analyst
Financial Analyst
Financial Analysts evaluate financial data to make investment recommendations. Word2Vec and Plotly can help you create interactive dashboards that can be used to track market trends, analyze company performance, and make informed investment decisions.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to measure the effectiveness of marketing campaigns and make recommendations for improvement. Word2Vec and Plotly can help you create interactive dashboards that can be used to track campaign performance, identify trends, and make data-driven decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. Word2Vec and Plotly can help you create interactive visualizations that can be used to analyze data, identify bottlenecks, and make recommendations for improvement.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks to an organization. Word2Vec and Plotly can help you create interactive dashboards that can be used to track risk exposure, identify trends, and make informed decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights for decision-making. Word2Vec and Plotly can help you create interactive visualizations that can be used to communicate your findings effectively.
For more career information including salaries, visit:
OpenCourser.com/course/ntts73/interactive
Reading list
We've selected eight 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
Interactive Word Embeddings using Word2Vec and Plotly.
Provides a comprehensive introduction to natural language processing (NLP), with a focus on practical applications using the Python programming language. It covers a wide range of NLP tasks, including text preprocessing, tokenization, stemming, lemmatization, parsing, and machine learning for NLP.
Save
Provides a practical guide to text mining using the R programming language. It covers a wide range of text mining tasks, including text preprocessing, tokenization, stemming, lemmatization, parsing, and machine learning for text mining.
Provides a comprehensive introduction to natural language processing (NLP). It covers a wide range of NLP tasks, including text preprocessing, tokenization, stemming, lemmatization, parsing, and machine learning for NLP.
Provides a comprehensive introduction to data analysis using the Python programming language. It covers a wide range of data analysis tasks, including data cleaning, data exploration, and data visualization.
Provides a comprehensive introduction to machine learning using the Python programming language. It covers a wide range of machine learning topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to deep learning using the Python programming language. It covers a wide range of deep learning topics, including convolutional neural networks, recurrent neural networks, and transformers.
Provides a comprehensive introduction to machine learning. It covers a wide range of machine learning topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive overview of deep learning techniques for natural language processing (NLP). It covers a wide range of topics, including word embeddings, recurrent neural networks, and transformers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ntts73/interactive
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
Builds a strong foundation for beginners, using a friendly and accessible approach that makes it perfect for those new to the field
Offers hands-on labs and interactive materials, providing learners with practical experience in applying the techniques they learn
Taught by Ari Anastassiou, an experienced instructor in the field, ensuring the course is up-to-date and relevant to industry needs
Examines a unique perspective on natural language processing, specifically in the context of recipe analysis
Develops skills and knowledge that are highly relevant to the food and grocery industry, including product development and marketing
Requires learners to come in with some basic knowledge of natural language processing and Python, which may be a barrier for some
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