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Sentiment Analysis with Deep Learning using BERT
Ari Anastassiou
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
What's inside
Syllabus
Sentiment Analysis with Deep Learning using BERT
In this 1.5-to-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 finetuning 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.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Bert-powered sentiment analysis
Learners say this course is a largely positive experience, with a course-associated project that is particularly well received. Students say the engaging assignments and the well-received instructor made this course a positive experience. According to students, the course starts with the basics and builds toward more complex activities so that learners grasp key features like lectures, readings, exams, quizzes, homework assignments, deadlines, and certificates, along with the fundamentals of sentiment analysis and deep learning using BERT.
Course is engaging
"Fun and knowledgeable Course "
"Awesome Project"
"Excellent course. Everything you need for the baseline."
Project is well-received
"Very brief and to the point"
"very clear explanation.. it easy to follow "
"Great Starting course on BERT."
Instructor is well-received
"The instructor is excellent."
"The instructor explains very well on how to using bert to train a sentiment classifier. Very cool project. "
"Great instructor! Comments about theory and shortcut commands just on point."
Insufficient explanations
"Need major improvement with the interactive notebook along with its response time and UI"
"He does not explain anything about the logic behind what he is doing."
"I really did not like using Rhyme. I preferred to. use the Jupyter notebook like in the other exercises."
Activities
Coming soon
We're preparing activities for
Sentiment Analysis with Deep Learning using BERT.
These are activities you can do either before, during, or after a course.
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.
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.
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.
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.
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.
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.
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.
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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Effort
120 minutes
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
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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