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Jay Alammar,
Arpan Chakraborty,
Luis Serrano,
and
Dana Sheahen
Register for this course and see more details by visiting:
OpenCourser.com/course/flny2d/sentiment
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What's inside
Syllabus
Sentiment Analysis with Andrew Trask
Implement a sentiment prediction RNN
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with an interest in sentiment analysis
Taught by experienced instructors in the field
Covers the implementation of sentiment prediction RNNs
Save this course
Save Sentiment Analysis Extras to your list so you can find it easily later:
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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 Extras with these
activities:
Deep Learning with Python
Show steps
This book covers the core concepts of deep learning, which is foundational to RNNs.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read the book
-
Do the exercises
TensorFlow tutorials
Show steps
These tutorials provide step-by-step instructions on how to use TensorFlow for RNNs.
Show steps
-
Choose a tutorial
-
Follow the instructions
-
Experiment with the code
RNN workshop
Show steps
This workshop will provide you with hands-on experience with RNNs.
Show steps
-
Register for the workshop
-
Attend the workshop
-
Participate in the exercises
Six other activities
Expand to see all activities and additional details
Show all nine activities
Coding Kata
Show steps
Writing and testing code repeatedly can help you practice the RNN implementation.
Show steps
LeetCode problems
Show steps
Solving coding problems will help you reinforce the concepts of RNNs.
Show steps
-
Pick a problem
-
Read the problem description
-
Write a solution
-
Test your solution
Study group
Show steps
Discussing concepts with peers can help you deepen your understanding of RNNs.
Show steps
-
Find a study group
-
Meet regularly
-
Discuss the material
Build a sentiment analysis model
Show steps
Building a model will help you synthesize concepts from the course.
Show steps
-
Choose a dataset
-
Preprocess the data
-
Build the model
-
Evaluate the model
Mentor other students
Show steps
Mentoring others will help you solidify your understanding of RNNs.
Show steps
-
Find a mentee
-
Meet regularly
-
Answer their questions
-
Provide guidance
Write a blog post
Show steps
Writing a blog post will help you synthesize the concepts and share your knowledge.
Show steps
-
Choose a topic
-
Write an outline
-
Write the post
-
Proofread and edit your post
Deep Learning with Python
Show steps
This book covers the core concepts of deep learning, which is foundational to RNNs.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Do the exercises
TensorFlow tutorials
Show steps
These tutorials provide step-by-step instructions on how to use TensorFlow for RNNs.
Show steps
Show steps
Show steps
- Choose a tutorial
- Follow the instructions
- Experiment with the code
RNN workshop
Show steps
This workshop will provide you with hands-on experience with RNNs.
Show steps
Show steps
Show steps
- Register for the workshop
- Attend the workshop
- Participate in the exercises
Six other activities
Expand to see all activities and additional details
Show all nine activities
Coding Kata
Show steps
Writing and testing code repeatedly can help you practice the RNN implementation.
Show steps
Show steps
Show steps
LeetCode problems
Show steps
Solving coding problems will help you reinforce the concepts of RNNs.
Show steps
Show steps
Show steps
- Pick a problem
- Read the problem description
- Write a solution
- Test your solution
Study group
Show steps
Discussing concepts with peers can help you deepen your understanding of RNNs.
Show steps
Show steps
Show steps
- Find a study group
- Meet regularly
- Discuss the material
Build a sentiment analysis model
Show steps
Building a model will help you synthesize concepts from the course.
Show steps
Show steps
Show steps
- Choose a dataset
- Preprocess the data
- Build the model
- Evaluate the model
Mentor other students
Show steps
Mentoring others will help you solidify your understanding of RNNs.
Show steps
Show steps
Show steps
- Find a mentee
- Meet regularly
- Answer their questions
- Provide guidance
Write a blog post
Show steps
Writing a blog post will help you synthesize the concepts and share your knowledge.
Show steps
Show steps
Show steps
- Choose a topic
- Write an outline
- Write the post
- Proofread and edit your post
Career center
Learners who complete Sentiment Analysis Extras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data using techniques such as machine learning, statistics, and predictive modeling. They work with large data sets and are essential in developing new products or services. The Sentiment Analysis Extras course can be beneficial for Data Scientists because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as product development and customer segmentation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists and other engineers to develop and deploy machine learning solutions. The Sentiment Analysis Extras course can be beneficial for Machine Learning Engineers because it provides them with the skills to build and train machine learning models. This can be useful for tasks such as developing new features for products or services.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. They work with data scientists and other analysts to develop insights that can be used to improve business decisions. The Sentiment Analysis Extras course can be beneficial for Data Analysts because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing insights for product development or customer segmentation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. The Sentiment Analysis Extras course can be beneficial for Software Engineers because it provides them with the skills to develop software that can analyze data and identify patterns. This can be useful for tasks such as developing software for data analysis or customer segmentation.
Product Manager
Product Manager
Product Managers develop and manage products. They work with a variety of stakeholders to define product requirements and ensure that products meet the needs of users. The Sentiment Analysis Extras course may be useful for Product Managers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as defining product requirements or developing product roadmaps.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency and effectiveness. They work with stakeholders to identify and define business requirements. The Sentiment Analysis Extras course may be useful for Business Analysts because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as identifying and defining business requirements.
Customer Success Manager
Customer Success Manager
Customer Success Managers work with customers to ensure that they are successful with a product or service. They provide support and guidance to customers and help them to achieve their goals. The Sentiment Analysis Extras course may be useful for Customer Success Managers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as identifying customers who are at risk of churn or developing strategies to improve customer satisfaction.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. They work with a variety of stakeholders to define marketing goals and ensure that campaigns are successful. The Sentiment Analysis Extras course may be useful for Marketing Managers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing marketing campaigns or measuring the success of campaigns.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. They work with a variety of departments to ensure that the business runs smoothly and efficiently. The Sentiment Analysis Extras course may be useful for Operations Managers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as improving operational efficiency or developing new processes.
Sales Manager
Sales Manager
Sales Managers lead and manage sales teams. They develop and execute sales strategies and ensure that sales teams are successful. The Sentiment Analysis Extras course may be useful for Sales Managers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing sales strategies or identifying new sales opportunities.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. They work with a variety of stakeholders to develop and execute investment strategies. The Sentiment Analysis Extras course may be useful for Financial Analysts because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing investment strategies or identifying undervalued stocks.
Consultant
Consultant
Consultants advise clients on a variety of business issues. They work with clients to identify and solve problems and develop and implement solutions. The Sentiment Analysis Extras course may be useful for Consultants because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as identifying and solving business problems or developing and implementing solutions.
Researcher
Researcher
Researchers conduct research to advance knowledge and understanding. They work with a variety of stakeholders to develop and execute research projects. The Sentiment Analysis Extras course may be useful for Researchers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing research projects or identifying new areas of research.
Teacher
Teacher
Teachers teach students at all levels of education. They develop and deliver lesson plans and assess student learning. The Sentiment Analysis Extras course may be useful for Teachers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing lesson plans or assessing student learning.
Writer
Writer
Writers create written content for a variety of purposes. They work with a variety of stakeholders to develop and execute writing projects. The Sentiment Analysis Extras course may be useful for Writers because it provides them with the skills to analyze data and identify patterns. This can be useful for tasks such as developing writing projects or identifying new writing opportunities.
For more career information including salaries, visit:
OpenCourser.com/course/flny2d/sentiment
Reading list
We've selected 11 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 Extras.
This textbook provides a comprehensive overview of natural language processing, covering topics such as natural language understanding, natural language generation, and machine learning for NLP. It contains a large number of exercises and examples, making it a great resource for both students and practitioners.
Provides a comprehensive overview of sentiment analysis and opinion mining, covering topics such as sentiment analysis techniques, opinion mining, and applications of sentiment analysis. It valuable resource for both researchers and practitioners in the field.
Save
Provides a comprehensive overview of machine learning for text, covering topics such as text classification, text clustering, and text summarization. It valuable resource for both researchers and practitioners in the field.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as word embeddings, recurrent neural networks, and transformers. It valuable resource for both researchers and practitioners in the field.
This textbook provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and natural language generation. It valuable resource for both students and practitioners in the field.
Provides a comprehensive overview of machine learning for NLP, covering topics such as text classification, text clustering, and text summarization. It valuable resource for both researchers and practitioners in the field.
Provides a comprehensive overview of statistical natural language processing, covering topics such as natural language understanding, natural language generation, and machine learning for NLP. It valuable resource for both researchers and practitioners in the field.
Save
Provides a comprehensive overview of text mining with R, covering topics such as text preprocessing, text analysis, and text visualization. It valuable resource for both researchers and practitioners in the field.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It valuable resource for both researchers and practitioners in the field.
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for both researchers and practitioners in the field.
Provides a comprehensive overview of machine learning for hackers, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for both researchers and practitioners in the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/flny2d/sentiment
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Time
120 hours
Level
Advanced
Via
Udacity
Instructors
Jay Alammar
Arpan Chakraborty
Luis Serrano
Dana Sheahen
Language
English
Good to know
Suitable for learners with an interest in sentiment analysis
Taught by experienced instructors in the field
Covers the implementation of sentiment prediction RNNs
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