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Amit Yadav
In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework.
Read more
In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework.
Read more
In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework.
You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, recurrent neural networks, and optimization algorithms like gradient descent but want to understand how to use the Tensorflow to start performing natural language processing tasks like text classification. You should also have some basic familiarity with TensorFlow.
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/r4ndb6/tweet
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Syllabus
Tweet Emotion Recognition with TensorFlow
In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework.
You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, recurrent neural networks, and optimization algorithms like gradient descent but want to understand how to use the Tensorflow to start performing natural language processing tasks like text classification. You should also have some basic familiarity with TensorFlow.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for experienced Python programmers
Helps learners understand how to use TensorFlow to perform natural language processing tasks like text classification
Covers fundamental concepts of neural networks, recurrent neural networks, and gradient descent
Provides hands-on experience with a practical, guided project
Current version of TensorFlow is used
Assumes learners have basic familiarity with TensorFlow
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Reviews summary
5-star tensorflow course
According to students, this course - Tweet Emotion Recognition with TensorFlow - is excellent and worth spending an hour on. Students describe engaging assignments and easy-to-follow lectures. Most find the course wonderful and very helpful for beginners, but some experience difficult technical issues.
Many students find this course to be a great introduction to TensorFlow.
"very helpful ...... for beginners"
"Nice project! Easy to follow, centered on the relevant."
Students highly rate the course content.
"Awesome"
"excellent"
"One of the best course for knowledge up gradation."
Some learners mention encountering technical difficulties.
"The code didn't work even in the notebook shared..."
"The grader has technical issues."
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 Tweet Emotion Recognition with TensorFlow with these
activities:
Primer on Neural Networks
Show steps
Refresh foundational knowledge of Neural Networks to expedite learning in this course.
Browse courses on
Neural Networks
Show steps
-
Review coursework or study materials on the basics of Neural Networks.
-
Do practice questions on Neural Networks.
Resources Compilation
Show steps
Organize and expand on course materials for future reference.
Browse courses on
Neural Networks
Show steps
-
Gather online resources, articles, and videos related to Neural Networks, TensorFlow, and Natural Language Processing.
-
Organize the resources into a structured format, such as a digital notebook or online repository.
-
Annotate the resources with brief summaries or notes for easy reference.
TensorFlow Tutorial
Show steps
Gain additional exposure to TensorFlow to bolster understanding.
Browse courses on
TensorFlow
Show steps
-
Seek out and follow a TensorFlow tutorial.
-
Complete the exercises and examples provided in the tutorial.
-
Explore additional resources on TensorFlow.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Compile Course Resources
Show steps
Organize course materials to maximize retention.
Show steps
-
Review and organize notes, assignments, and quizzes from the course.
-
Create a system for categorizing and storing the materials.
-
Regularly review the compiled materials to reinforce learning.
Attend an AI Meetup
Show steps
Connect with others interested in AI to expand knowledge and stay updated.
Browse courses on
AI
Show steps
-
Identify and attend an AI meetup in your area.
-
Engage in discussions and ask questions about Tweet Emotion Recognition and related topics.
-
Exchange contact information with other attendees for future collaboration.
Tweet Emotion Recognition Exercises
Show steps
Reinforce understanding of Tweet Emotion Recognition through targeted drills.
Show steps
-
Access online resources or textbooks that provide practice exercises or problems.
-
Solve the problems and check your solutions against provided answer keys.
-
Repeat the process for multiple exercises to build proficiency.
Lead a Study Group
Show steps
Reinforce learning by sharing knowledge with others.
Browse courses on
Neural Networks
Show steps
-
Offer to lead a study group for your classmates or peers.
-
Prepare materials and activities to facilitate discussions on course-related topics.
-
Guide discussions and answer questions, fostering a collaborative learning environment.
Mini Text Classification Project
Show steps
Solidify knowledge through practical application in a project.
Browse courses on
Text Classification
Show steps
-
Choose a small text classification dataset.
-
Develop a text classification model using TensorFlow.
-
Evaluate the model's performance.
-
Document your approach and results.
Contribute to TensorFlow Community
Show steps
Apply knowledge and expand understanding through open-source contributions.
Browse courses on
TensorFlow
Show steps
-
Explore TensorFlow's GitHub repository and identify areas for contribution.
-
Choose a project or issue to work on.
-
Make a code contribution, such as fixing a bug or adding a feature.
-
Collaborate with the TensorFlow community through code reviews and discussions.
Primer on Neural Networks
Show steps
Refresh foundational knowledge of Neural Networks to expedite learning in this course.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Review coursework or study materials on the basics of Neural Networks.
- Do practice questions on Neural Networks.
Resources Compilation
Show steps
Organize and expand on course materials for future reference.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Gather online resources, articles, and videos related to Neural Networks, TensorFlow, and Natural Language Processing.
- Organize the resources into a structured format, such as a digital notebook or online repository.
- Annotate the resources with brief summaries or notes for easy reference.
TensorFlow Tutorial
Show steps
Gain additional exposure to TensorFlow to bolster understanding.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Seek out and follow a TensorFlow tutorial.
- Complete the exercises and examples provided in the tutorial.
- Explore additional resources on TensorFlow.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Compile Course Resources
Show steps
Organize course materials to maximize retention.
Show steps
Show steps
Show steps
- Review and organize notes, assignments, and quizzes from the course.
- Create a system for categorizing and storing the materials.
- Regularly review the compiled materials to reinforce learning.
Attend an AI Meetup
Show steps
Connect with others interested in AI to expand knowledge and stay updated.
Browse courses on
AI
Show steps
Show steps
Show steps
- Identify and attend an AI meetup in your area.
- Engage in discussions and ask questions about Tweet Emotion Recognition and related topics.
- Exchange contact information with other attendees for future collaboration.
Tweet Emotion Recognition Exercises
Show steps
Reinforce understanding of Tweet Emotion Recognition through targeted drills.
Show steps
Show steps
Show steps
- Access online resources or textbooks that provide practice exercises or problems.
- Solve the problems and check your solutions against provided answer keys.
- Repeat the process for multiple exercises to build proficiency.
Lead a Study Group
Show steps
Reinforce learning by sharing knowledge with others.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Offer to lead a study group for your classmates or peers.
- Prepare materials and activities to facilitate discussions on course-related topics.
- Guide discussions and answer questions, fostering a collaborative learning environment.
Mini Text Classification Project
Show steps
Solidify knowledge through practical application in a project.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Choose a small text classification dataset.
- Develop a text classification model using TensorFlow.
- Evaluate the model's performance.
- Document your approach and results.
Contribute to TensorFlow Community
Show steps
Apply knowledge and expand understanding through open-source contributions.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Explore TensorFlow's GitHub repository and identify areas for contribution.
- Choose a project or issue to work on.
- Make a code contribution, such as fixing a bug or adding a feature.
- Collaborate with the TensorFlow community through code reviews and discussions.
Career center
Learners who complete Tweet Emotion Recognition with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and deploy machine learning models that can understand and generate human language. They work in a variety of industries, including technology, finance, and healthcare. This course in Tweet Emotion Recognition with TensorFlow provides a strong foundation for a career as a Natural Language Processing Engineer. It teaches students how to use TensorFlow to build and train neural networks for natural language processing tasks. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Data Analyst
Data Analyst
Data Analysts use their knowledge of mathematics, computer programming, and statistical modeling to help organizations make better decisions. They are employed in a variety of industries, including technology, finance, healthcare, and manufacturing. This course in Tweet Emotion Recognition with TensorFlow provides a strong foundation for a career as a Data Analyst. It teaches students how to use TensorFlow, a popular machine learning framework, to build and train neural networks. Neural networks are used in a wide range of applications, including natural language processing, image recognition, and speech recognition. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, computer programming, and statistical modeling to help organizations make better decisions. They are employed in a variety of industries, including technology, finance, healthcare, and manufacturing. This course in Tweet Emotion Recognition with TensorFlow provides a strong foundation for a career as a Data Scientist. It teaches students how to use TensorFlow, a popular machine learning framework, to build and train neural networks. Neural networks are used in a wide range of applications, including natural language processing, image recognition, and speech recognition. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. They are employed in a variety of settings, including universities, research labs, and corporations. This course in Tweet Emotion Recognition with TensorFlow provides a valuable introduction to TensorFlow. TensorFlow is one of the most popular machine learning frameworks and is used by a wide range of organizations, including Google, Amazon, and Microsoft. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, computer programming, and statistical modeling to help organizations make better decisions. They are employed in a variety of industries, including finance, healthcare, and manufacturing. This course in Tweet Emotion Recognition with TensorFlow provides a valuable introduction to TensorFlow. TensorFlow is one of the most popular machine learning frameworks and is used by a wide range of organizations, including Google, Amazon, and Microsoft. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including technology, finance, and healthcare. This course in Tweet Emotion Recognition with TensorFlow provides a valuable introduction to TensorFlow. TensorFlow is one of the most popular machine learning frameworks and is used by a wide range of organizations, including Google, Amazon, and Microsoft. By taking this course, students will learn the skills they need to develop and deploy software systems that can help businesses improve their operations.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. They work closely with Data Scientists to design and implement machine learning solutions that meet the needs of their organizations. This course in Tweet Emotion Recognition with TensorFlow provides a valuable introduction to TensorFlow. TensorFlow is one of the most popular machine learning frameworks and is used by a wide range of organizations, including Google, Amazon, and Microsoft. By taking this course, students will learn the skills they need to develop and deploy machine learning models that can help businesses improve their operations.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business processes and technology to help organizations improve their operations. They are employed in a variety of industries, including technology, finance, and healthcare. This course in Tweet Emotion Recognition with TensorFlow may be useful for Business Analysts who want to learn more about how machine learning can be used to improve business processes. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and accounting to help organizations make better decisions. They are employed in a variety of industries, including technology, finance, healthcare, and manufacturing. This course in Tweet Emotion Recognition with TensorFlow may be useful for Financial Analysts who want to learn more about how machine learning can be used to improve financial analysis processes. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Operations Manager
Operations Manager
Operations Managers are responsible for planning, developing, and implementing operations strategies. They work closely with other departments to ensure that operations are efficient and effective. This course in Tweet Emotion Recognition with TensorFlow may be useful for Operations Managers who want to learn more about how machine learning can be used to improve operations processes. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They work with customers to identify and meet their needs and develop and implement sales strategies. This course in Tweet Emotion Recognition with TensorFlow may be useful for Sales Managers who want to learn more about how machine learning can be used to improve sales processes. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for planning, developing, and implementing human resources strategies. They work closely with other departments to ensure that human resources are used effectively. This course in Tweet Emotion Recognition with TensorFlow may be useful for Human Resources Managers who want to learn more about how machine learning can be used to improve human resources processes. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching new products. They work closely with engineers, designers, and marketers to ensure that products meet the needs of customers. This course in Tweet Emotion Recognition with TensorFlow may be useful for Product Managers who want to learn more about how machine learning can be used to improve product development. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. They work with customers to identify and resolve problems and develop and implement customer success strategies. This course in Tweet Emotion Recognition with TensorFlow may be useful for Customer Success Managers who want to learn more about how machine learning can be used to improve customer success processes. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for planning, developing, and implementing marketing campaigns. They work closely with product managers, sales teams, and customers to ensure that products and services are marketed effectively. This course in Tweet Emotion Recognition with TensorFlow may be useful for Marketing Managers who want to learn more about how machine learning can be used to improve marketing campaigns. By taking this course, students will learn the skills they need to develop and deploy machine learning solutions that can help businesses improve their operations.
For more career information including salaries, visit:
OpenCourser.com/course/r4ndb6/tweet
Reading list
We've selected 12 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
Tweet Emotion Recognition with TensorFlow.
Provides a technical deep dive into recurrent neural networks, including their architecture, training algorithms, and applications. It valuable reference for learners who want to understand the inner workings of the models used in the course.
Save
Provides a comprehensive overview of deep learning concepts and techniques. It valuable reference for understanding the theoretical foundations of the course.
Focuses specifically on natural language processing tasks using TensorFlow, including text classification. It provides practical examples and code snippets that can be applied to the course project.
Provides a comprehensive overview of natural language processing concepts and techniques using Python. It covers a wide range of topics, including text classification and recurrent neural networks.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular Python library for natural language processing. It valuable reference for learners who want to use NLTK in their projects.
Provides a comprehensive overview of machine learning concepts and techniques using Python. It covers a wide range of topics, including text classification and recurrent neural networks.
Provides practical examples and code snippets for natural language processing tasks using Python. It covers a range of topics, including text classification and recurrent neural networks.
Provides a collection of recipes and code snippets for TensorFlow 2.0. It useful reference for learners who want to quickly find solutions to common TensorFlow problems.
Save
Provides a comprehensive overview of text mining techniques using R. It covers a wide range of topics, including text classification and sentiment analysis.
Provides a broad overview of deep learning concepts and techniques, including recurrent neural networks. It valuable reference for understanding the theoretical foundations of the course.
Provides a practical guide to machine learning algorithms and techniques. It good starting point for learners who are new to the field and want to build a foundation for the course.
Provides a broad overview of natural language processing concepts and techniques. It good starting point for learners who are new to the field and want to build a foundation for the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/r4ndb6/tweet
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Suitable for experienced Python programmers
Helps learners understand how to use TensorFlow to perform natural language processing tasks like text classification
Covers fundamental concepts of neural networks, recurrent neural networks, and gradient descent
Provides hands-on experience with a practical, guided project
Current version of TensorFlow is used
Assumes learners have basic familiarity with TensorFlow
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