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Mo Rebaie
This guided project course is part of the "Tensorflow for Natural Language Processing" series, and this series presents material that builds on the third course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
In this 2-hour long project-based course, you will learn the fundamentals of Text Embedding and Text Classification, and you will learn practically how to use text embeddings for a classification task in the real world and create, train, and test a neural network with Tensorflow using texts, and you will get a bonus...
Read more
This guided project course is part of the "Tensorflow for Natural Language Processing" series, and this series presents material that builds on the third course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
In this 2-hour long project-based course, you will learn the fundamentals of Text Embedding and Text Classification, and you will learn practically how to use text embeddings for a classification task in the real world and create, train, and test a neural network with Tensorflow using texts, and you will get a bonus...
Read more
This guided project course is part of the "Tensorflow for Natural Language Processing" series, and this series presents material that builds on the third course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
In this 2-hour long project-based course, you will learn the fundamentals of Text Embedding and Text Classification, and you will learn practically how to use text embeddings for a classification task in the real world and create, train, and test a neural network with Tensorflow using texts, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned text embedding and created a neural network with TensorFlow on text classification.
This class is for learners who want to learn how to work with natural language processing and use Python for building neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training text classification models and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.
Register for this course and see more details by visiting:
OpenCourser.com/course/rqhg9z/tensorflow
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes practical application, helping learners immediately apply their acquired skills
Builds core skills in text embedding and text classification, valuable for advancing a career in natural language processing
Taught by Mo Rebaie, who is highly recognized in the field of natural language processing
Course materials are presented clearly and understandably
Part of a series, indicating the opportunity for further learning and skill development
Requires learners to come in with basic deep learning knowledge, potentially limiting accessibility
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Reviews summary
Tensorflow text embedding project
In this TensorFlow project, you will put your learning into practice and create a neural network for text classification. This project will exercise your knowledge of natural language processing, Python, and neural networks. Note that some students found the exercises a bit basic.
Apply your NLP knowledge to a practical project.
"This guided project course ... will help learners reinforce their skills and build more projects with Tensorflow."
Get hands-on experience with TensorFlow and NLP.
"In this 2-hour long project-based course, you will learn the fundamentals of Text Embedding and Text Classification, and you will learn practically how to use text embeddings ... and create, train, and test a neural network with Tensorflow using texts."
There is a scientific error in one of the videos.
"Scientific error in video 4: The model uses 1 dense layer of 3 neurons, instead of 3 layers."
Some reviewers feel the exercises are too basic.
"Very very basic coding and technical Introduction to NLP."
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 TensorFlow for NLP: Text Embedding and Classification with these
activities:
Review Natural Language Processing with Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Show steps
Reinforce your understanding of the theoretical foundations of natural language processing with deep learning.
View
Deep Learning
on Amazon
Show steps
-
Read the book's introduction and first chapter.
-
Summarize the key concepts and techniques discussed in the book.
-
Identify the connections between the concepts discussed in the book and the material covered in the course.
Create a collection of resources on natural language processing
Show steps
Organize and share valuable information and materials related to natural language processing.
Browse courses on
Natural Language Processing
Show steps
-
Gather resources such as articles, tutorials, videos, and tools related to natural language processing.
-
Organize the resources into a collection using a platform such as a blog, website, or online repository.
-
Share the collection with other learners and the natural language processing community.
Solve coding challenges related to natural language processing
Show steps
Strengthen your coding skills and understanding of natural language processing concepts by solving coding challenges.
Browse courses on
Natural Language Processing
Show steps
-
Find online coding challenges or practice problems related to natural language processing.
-
Attempt to solve the challenges on your own.
-
Review and analyze your solutions, identifying areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group or discussion forum on natural language processing
Show steps
Engage with other learners to discuss natural language processing concepts and share knowledge.
Browse courses on
Natural Language Processing
Show steps
-
Join an online or in-person study group or discussion forum dedicated to natural language processing.
-
Actively participate in discussions and share your insights.
-
Help other learners by answering their questions and providing support.
Build a text classification model using TensorFlow
Show steps
Apply your knowledge of TensorFlow and text classification to a practical project.
Browse courses on
TensorFlow
Show steps
-
Gather a dataset of text data.
-
Preprocess the data by cleaning and tokenizing it.
-
Build a TensorFlow model for text classification.
-
Train and evaluate the model on the dataset.
-
Deploy the model to a production environment.
Follow tutorials on advanced natural language processing techniques
Show steps
Expand your knowledge of natural language processing by exploring advanced techniques through guided tutorials.
Browse courses on
Natural Language Processing
Show steps
-
Identify advanced natural language processing techniques that you are interested in learning.
-
Find online tutorials or courses that cover these techniques.
-
Follow the tutorials and complete the exercises provided.
Contribute to an open-source natural language processing project
Show steps
Gain practical experience and contribute to the natural language processing community by working on open-source projects.
Browse courses on
Natural Language Processing
Show steps
-
Identify an open-source natural language processing project that you are interested in contributing to.
-
熟悉项目的代码库和贡献指南.
-
Make a contribution to the project, such as fixing a bug or adding a new feature.
Attend a natural language processing workshop or conference
Show steps
Connect with experts, learn about the latest advancements, and network with other professionals in the field of natural language processing.
Browse courses on
Natural Language Processing
Show steps
-
Identify relevant natural language processing workshops or conferences.
-
Register and attend the event.
-
Actively participate in sessions, ask questions, and network with other attendees.
Review Natural Language Processing with Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Show steps
Reinforce your understanding of the theoretical foundations of natural language processing with deep learning.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter.
- Summarize the key concepts and techniques discussed in the book.
- Identify the connections between the concepts discussed in the book and the material covered in the course.
Create a collection of resources on natural language processing
Show steps
Organize and share valuable information and materials related to natural language processing.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Gather resources such as articles, tutorials, videos, and tools related to natural language processing.
- Organize the resources into a collection using a platform such as a blog, website, or online repository.
- Share the collection with other learners and the natural language processing community.
Solve coding challenges related to natural language processing
Show steps
Strengthen your coding skills and understanding of natural language processing concepts by solving coding challenges.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Find online coding challenges or practice problems related to natural language processing.
- Attempt to solve the challenges on your own.
- Review and analyze your solutions, identifying areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group or discussion forum on natural language processing
Show steps
Engage with other learners to discuss natural language processing concepts and share knowledge.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Join an online or in-person study group or discussion forum dedicated to natural language processing.
- Actively participate in discussions and share your insights.
- Help other learners by answering their questions and providing support.
Build a text classification model using TensorFlow
Show steps
Apply your knowledge of TensorFlow and text classification to a practical project.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Gather a dataset of text data.
- Preprocess the data by cleaning and tokenizing it.
- Build a TensorFlow model for text classification.
- Train and evaluate the model on the dataset.
- Deploy the model to a production environment.
Follow tutorials on advanced natural language processing techniques
Show steps
Expand your knowledge of natural language processing by exploring advanced techniques through guided tutorials.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify advanced natural language processing techniques that you are interested in learning.
- Find online tutorials or courses that cover these techniques.
- Follow the tutorials and complete the exercises provided.
Contribute to an open-source natural language processing project
Show steps
Gain practical experience and contribute to the natural language processing community by working on open-source projects.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify an open-source natural language processing project that you are interested in contributing to.
- 熟悉项目的代码库和贡献指南.
- Make a contribution to the project, such as fixing a bug or adding a new feature.
Attend a natural language processing workshop or conference
Show steps
Connect with experts, learn about the latest advancements, and network with other professionals in the field of natural language processing.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify relevant natural language processing workshops or conferences.
- Register and attend the event.
- Actively participate in sessions, ask questions, and network with other attendees.
Career center
Learners who complete TensorFlow for NLP: Text Embedding and Classification will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course provides a strong foundation in text embedding and classification, which are essential skills for Machine Learning Engineers working with natural language data. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course provides a strong foundation in text embedding and classification, which are essential skills for Data Scientists working with natural language data. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs and develops systems that can understand and generate human language. This course provides a strong foundation in text embedding and classification, which are essential skills for Natural Language Processing Engineers. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides a strong foundation in text embedding and classification, which are essential skills for Software Engineers working on natural language processing projects. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Research Scientist
Research Scientist
A Research Scientist conducts research in a variety of fields, including natural language processing. This course provides a strong foundation in text embedding and classification, which are essential skills for Research Scientists working on natural language processing projects. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Data Analyst
Data Analyst
A Data Analyst analyzes data to identify trends and patterns. This course provides a strong foundation in text embedding and classification, which are essential skills for Data Analysts working with natural language data. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes to identify areas for improvement. This course provides a strong foundation in text embedding and classification, which are essential skills for Business Analysts working on natural language processing projects. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Product Manager
Product Manager
A Product Manager develops and manages products. This course provides a strong foundation in text embedding and classification, which are essential skills for Product Managers working on natural language processing products. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. This course provides a strong foundation in text embedding and classification, which are essential skills for Marketing Managers working on natural language processing campaigns. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Sales Manager
Sales Manager
A Sales Manager develops and executes sales strategies. This course provides a strong foundation in text embedding and classification, which are essential skills for Sales Managers working on natural language processing projects. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Customer Success Manager
Customer Success Manager
A Customer Success Manager helps customers achieve success with a product or service. This course provides a strong foundation in text embedding and classification, which are essential skills for Customer Success Managers working on natural language processing products or services. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. This course provides a strong foundation in text embedding and classification, which are essential skills for Technical Writers working on natural language processing documentation. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Content Writer
Content Writer
A Content Writer creates and maintains content for websites, blogs, and other platforms. This course provides a strong foundation in text embedding and classification, which are essential skills for Content Writers working on natural language processing content. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
Copywriter
Copywriter
A Copywriter creates and maintains copy for marketing and advertising campaigns. This course provides a strong foundation in text embedding and classification, which are essential skills for Copywriters working on natural language processing campaigns. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
UX Writer
UX Writer
A UX Writer creates and maintains user experience (UX) content for websites, apps, and other digital products. This course provides a strong foundation in text embedding and classification, which are essential skills for UX Writers working on natural language processing UX content. By completing this course, learners will gain the knowledge and skills needed to build and train text classification models using TensorFlow, a popular deep learning library.
For more career information including salaries, visit:
OpenCourser.com/course/rqhg9z/tensorflow
Reading list
We've selected ten 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
TensorFlow for NLP: Text Embedding and Classification.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, including statistical NLP, machine learning for NLP, and deep learning for NLP. This book is written for intermediate and advanced NLP practitioners.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and speech synthesis. This book is written for intermediate and advanced NLP practitioners.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including word embeddings, recurrent neural networks, and convolutional neural networks. This book is written for intermediate and advanced NLP practitioners.
Save
Provides a comprehensive overview of probabilistic natural language processing. It covers a wide range of topics, including probabilistic models for text, natural language understanding, and natural language generation. This book is written for intermediate and advanced NLP practitioners.
Save
Provides a comprehensive overview of machine learning for text. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. This book is written for intermediate and advanced NLP practitioners.
Provides a comprehensive overview of natural language processing with a focus on using the Natural Language Toolkit (NLTK) library in Python. It covers a wide range of NLP tasks, including text classification, sentiment analysis, and named entity recognition. While this book is written for beginners, it valuable reference for NLP practitioners at all levels.
Provides a comprehensive overview of natural language processing with a focus on using the Natural Language Toolkit (NLTK) library in Python. It covers a wide range of NLP tasks, including text classification, sentiment analysis, and named entity recognition. This book is written for beginners and intermediate NLP practitioners.
Provides a practical introduction to natural language processing for the social sciences. It covers a wide range of NLP tasks, including text classification, sentiment analysis, and topic modeling. This book is written for beginners and intermediate NLP practitioners.
Save
Provides a practical introduction to text mining with R. It covers a wide range of text mining tasks, including text preprocessing, text classification, and sentiment analysis. This book is written for beginners and intermediate NLP practitioners.
Provides a practical introduction to text analytics with Python. It covers a wide range of text analytics tasks, including text preprocessing, text classification, and sentiment analysis. This book is written for beginners and intermediate NLP practitioners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rqhg9z/tensorflow
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Good to know
Emphasizes practical application, helping learners immediately apply their acquired skills
Builds core skills in text embedding and text classification, valuable for advancing a career in natural language processing
Taught by Mo Rebaie, who is highly recognized in the field of natural language processing
Course materials are presented clearly and understandably
Part of a series, indicating the opportunity for further learning and skill development
Requires learners to come in with basic deep learning knowledge, potentially limiting accessibility
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