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Ari Anastassiou
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.
Read more
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.
Read more
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.
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/95bl2r/sentiment
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces skills, knowledge, and tools necessary for sentiment analysis with Deep Learning
Utilizes PyTorch BERT model to delve into the topic of multi-class classification
Provides guidance on optimizing and scheduling models to optimize training performance
Trains students on designing training and evaluation loops to monitor model performance during training
Enables students to create sentiment analysis models leveraging BERT's immense language knowledge
Course duration is relatively short, allowing for quick completion
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Reviews summary
Practical bert sentiment analysis project
According to students, this course offers a highly practical and effective guide to sentiment analysis using BERT and PyTorch. Learners consistently praise its hands-on, project-based approach, which helps solidify understanding of fine-tuning deep learning models for multi-class classification. While many find it a useful and concise experience, the course has a fast pace and assumes prior knowledge of PyTorch and deep learning fundamentals, making it less suitable for absolute beginners. Additionally, some learners reported technical issues with the lab environment, particularly outside the North America region. Despite these points, it is generally considered a valuable practical stepping stone for applying BERT.
A fast-paced project, ideal for quick skill acquisition.
"A concise and practical guide... My only minor gripe is the speed; it's quite fast-paced."
"The content itself is good, but it rushes through several critical PyTorch concepts."
"It's a fast-paced project that gets straight to the point, which I appreciate."
Offers a hands-on approach to applying BERT for sentiment analysis.
"This project was exactly what I needed to get my hands dirty with BERT fine-tuning."
"Absolutely brilliant for practical application. I was able to replicate the results and adapt the code for my own dataset."
"I learned how to apply BERT to a real-world sentiment task in just under 2 hours. Super useful!"
Varied experiences with the coding environment, some facing issues.
"I had issues with the lab environment repeatedly. It was frustrating and made the 2-hour project stretch much longer."
"The regional restriction mentioned in the description is real. I struggled constantly with the lab environment outside North America."
"The course environment worked seamlessly for me, which was a relief. This was a concern from older reviews."
Requires prior knowledge of PyTorch and deep learning concepts.
"It assumes you know PyTorch already, so if you're a complete beginner, it might be a bit fast."
"I felt lost from the start as someone new to deep learning. This is NOT for beginners."
"This course is for implementation, not foundational understanding, best for those who know the 'why'."
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 Sentiment Analysis with Deep Learning using BERT with these
activities:
Review PyTorch
Show steps
Begin preparing for this course by reviewing PyTorch, the deep learning library focus of this course. This review will solidify your foundation to prepare you for success when the coursework begins in earnest.
Browse courses on
PyTorch
Show steps
-
Read and understand the PyTorch documentation
-
Complete PyTorch tutorials for beginners
-
Review PyTorch examples on GitHub
Find a Mentor in the Industry
Show steps
Accelerate your learning and career growth by finding a mentor in the NLP industry. A mentor can provide you with valuable insights, guidance, and support.
Browse courses on
Mentoring
Show steps
-
Identify potential mentors through LinkedIn or industry events
-
Reach out and introduce yourself
Follow Hugging Face Tutorials
Show steps
Supplement your coursework by completing tutorials from Hugging Face, the company that developed Transformers. These tutorials will provide you with valuable hands-on experience with BERT.
Browse courses on
Hugging Face
Show steps
-
Complete the Hugging Face BERT tutorial
-
Explore other Hugging Face tutorials related to BERT
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Show steps
Enhance your learning by joining a study group with other students enrolled in this course. Collaborating with peers will provide you with diverse perspectives and opportunities to clarify concepts.
Show steps
-
Find a study group on online forums
-
Organize regular study sessions
-
Discuss course materials and share insights
Complete Coding Exercises
Show steps
Reinforce your understanding of BERT and natural language processing by completing coding exercises. These exercises will test your skills and help you identify areas where you need additional practice.
Browse courses on
Python
Show steps
-
Solve coding exercises on Kaggle
-
Complete coding challenges on HackerRank
Participate in a Kaggle Competition
Show steps
Challenge yourself and showcase your skills by participating in a Kaggle competition related to sentiment analysis or BERT. This will provide you with an opportunity to apply your knowledge and potentially earn recognition for your work.
Browse courses on
Kaggle
Show steps
-
Identify and join a relevant competition
-
Develop and submit your solution
-
Analyze your results and learn from feedback
Build a Sentiment Analysis App
Show steps
Deepen your understanding of sentiment analysis by building a practical application. This project will challenge you to apply the concepts you learn in class and develop a valuable skill.
Browse courses on
BERT
Show steps
-
Design and plan the app's functionality
-
Develop the app using a suitable framework
-
Deploy the app and evaluate its performance
Mentor Junior Students
Show steps
Reinforce your understanding of BERT and NLP by mentoring junior students. This will provide you with an opportunity to clarify concepts, share your knowledge, and develop your leadership skills.
Browse courses on
Mentoring
Show steps
-
Identify and connect with junior students seeking mentorship
-
Establish regular mentoring sessions
-
Provide guidance and support in understanding BERT and NLP
Review PyTorch
Show steps
Begin preparing for this course by reviewing PyTorch, the deep learning library focus of this course. This review will solidify your foundation to prepare you for success when the coursework begins in earnest.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Read and understand the PyTorch documentation
- Complete PyTorch tutorials for beginners
- Review PyTorch examples on GitHub
Find a Mentor in the Industry
Show steps
Accelerate your learning and career growth by finding a mentor in the NLP industry. A mentor can provide you with valuable insights, guidance, and support.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify potential mentors through LinkedIn or industry events
- Reach out and introduce yourself
Follow Hugging Face Tutorials
Show steps
Supplement your coursework by completing tutorials from Hugging Face, the company that developed Transformers. These tutorials will provide you with valuable hands-on experience with BERT.
Browse courses on
Hugging Face
Show steps
Show steps
Show steps
- Complete the Hugging Face BERT tutorial
- Explore other Hugging Face tutorials related to BERT
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Show steps
Enhance your learning by joining a study group with other students enrolled in this course. Collaborating with peers will provide you with diverse perspectives and opportunities to clarify concepts.
Show steps
Show steps
Show steps
- Find a study group on online forums
- Organize regular study sessions
- Discuss course materials and share insights
Complete Coding Exercises
Show steps
Reinforce your understanding of BERT and natural language processing by completing coding exercises. These exercises will test your skills and help you identify areas where you need additional practice.
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve coding exercises on Kaggle
- Complete coding challenges on HackerRank
Participate in a Kaggle Competition
Show steps
Challenge yourself and showcase your skills by participating in a Kaggle competition related to sentiment analysis or BERT. This will provide you with an opportunity to apply your knowledge and potentially earn recognition for your work.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify and join a relevant competition
- Develop and submit your solution
- Analyze your results and learn from feedback
Build a Sentiment Analysis App
Show steps
Deepen your understanding of sentiment analysis by building a practical application. This project will challenge you to apply the concepts you learn in class and develop a valuable skill.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Design and plan the app's functionality
- Develop the app using a suitable framework
- Deploy the app and evaluate its performance
Mentor Junior Students
Show steps
Reinforce your understanding of BERT and NLP by mentoring junior students. This will provide you with an opportunity to clarify concepts, share your knowledge, and develop your leadership skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify and connect with junior students seeking mentorship
- Establish regular mentoring sessions
- Provide guidance and support in understanding BERT and NLP
Career center
Learners who complete Sentiment Analysis with Deep Learning using BERT 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 maintain systems that can understand and generate human language. Sentiment analysis is a key application of natural language processing. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Natural Language Processing Engineers build and improve sentiment analysis systems.
Data Analyst
Data Analyst
Data Analysts analyze data to extract meaningful insights. These insights help businesses make more informed decisions. Sentiment analysis is a type of data analysis. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Data Analysts build and improve sentiment analysis models.
Business Analyst
Business Analyst
Business Analysts analyze business processes to identify areas for improvement. Sentiment analysis can be used to understand customer feedback and improve business processes. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Business Analysts build and improve sentiment analysis models to support business decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. Sentiment analysis is a common application of machine learning. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. These skills are essential for Machine Learning Engineers working on sentiment analysis projects.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user experiences for products and services. Sentiment analysis can be used to understand user feedback and improve user experiences. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help User Experience Designers build and improve sentiment analysis models to support user experience design decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Sentiment analysis is a common application of software engineering. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. These skills can help Software Engineers build and improve sentiment analysis software systems.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. Sentiment analysis can be used to understand customer feedback and improve marketing campaigns. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Marketing Managers build and improve sentiment analysis models to support marketing decisions.
Product Manager
Product Manager
Product Managers develop and manage products. Sentiment analysis can be used to understand customer feedback and improve products. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Product Managers build and improve sentiment analysis models to support product decisions.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers achieve success with a company's products or services. Sentiment analysis can be used to understand customer feedback and improve customer success efforts. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Customer Success Managers build and improve sentiment analysis models to support customer success initiatives.
Sales Manager
Sales Manager
Sales Managers lead and manage sales teams. Sentiment analysis can be used to understand customer feedback and improve sales strategies. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Sales Managers build and improve sentiment analysis models to support sales decisions.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. Sentiment analysis can be used to understand customer feedback and improve operations. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Operations Managers build and improve sentiment analysis models to support operational decisions.
Project Manager
Project Manager
Project Managers plan and execute projects. Sentiment analysis can be used to understand customer feedback and improve project outcomes. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Project Managers build and improve sentiment analysis models to support project decisions.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. Sentiment analysis can be used to analyze customer feedback on technical documentation. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Technical Writers build and improve sentiment analysis models to support documentation decisions.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software to ensure it meets quality standards. Sentiment analysis can be used to analyze customer feedback on software quality. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Quality Assurance Analysts build and improve sentiment analysis models to support software quality assurance efforts.
Data Scientist
Data Scientist
Data Scientists analyze data with a variety of techniques to extract meaningful insights. These data-driven insights help businesses make more informed decisions. The course, Sentiment Analysis with Deep Learning using BERT, may be useful for this career as it provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. These techniques are commonly used by Data Scientists for sentiment analysis in various industries.
For more career information including salaries, visit:
OpenCourser.com/course/95bl2r/sentiment
Reading list
We've selected nine 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
Sentiment Analysis with Deep Learning using BERT.
Focuses on practical NLP tasks using PyTorch, including sentiment analysis. It provides step-by-step instructions and code examples, making it a useful resource for applying BERT in real-world scenarios.
Provides a solid foundation in statistical learning, which is essential for understanding the statistical principles behind NLP models like BERT. It covers topics such as regression, classification, and dimensionality reduction.
Save
This classic textbook provides a comprehensive overview of speech and language processing, including NLP. It covers a wide range of topics, including sentiment analysis, and offers a solid foundation for understanding the field.
Provides a more theoretical perspective on NLP, covering topics such as semantics, pragmatics, and discourse. While it does not focus on BERT or deep learning, it offers a valuable foundation for understanding the linguistic principles behind NLP.
Provides a comprehensive introduction to statistical NLP, covering topics such as language modeling, machine translation, and information retrieval. While it does not cover BERT specifically, it offers a solid foundation for understanding the statistical techniques used in NLP.
Save
Provides a comprehensive overview of deep learning, including its theoretical foundations and applications. While it does not cover NLP specifically, it offers a valuable foundation for understanding the deep learning techniques used in BERT.
This classic textbook provides a comprehensive overview of artificial intelligence, including NLP. While it does not cover BERT specifically, it offers a valuable foundation for understanding the broader context of NLP within AI.
Provides a comprehensive overview of statistical learning, covering topics such as regression, classification, and dimensionality reduction. While it does not cover NLP specifically, it offers a valuable foundation for understanding the statistical techniques used in NLP.
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning. While it does not cover NLP or BERT specifically, it offers a valuable foundation for understanding the broader context of NLP within machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/95bl2r/sentiment
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