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Ryan Ahmed
In this hands-on project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people’s sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities.
Note: This course works...
Read more
In this hands-on project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people’s sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities.
Note: This course works...
Read more
In this hands-on project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people’s sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities.
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/2by9xb/natural
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes sentiment analysis, a crucial NLP technique used in many industries
Utilizes LSTM deep learning models, a leading technology in NLP
Provides hands-on experience through a practical project, allowing learners to apply their knowledge
Taught by industry expert Ryan Ahmed, who brings real-world insights
Focuses on stock sentiment analysis, which has significant applications in finance and investing
Note that course accessibility is currently limited to the North America region
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Reviews summary
Nlp for stocks news analysis
This NLP course is well-received by students. It provides a good introduction to the topic and is well-facilitated. Students have found the use cases and environment to be excellent. However, some students have noted that the course is currently limited to learners in North America.
Provides a good introduction to NLP for stocks news analysis.
"Nice introduction class. "
Excellent facilitation, use cases, and environment.
"Excellent facilitation,usecase and environment."
Currently limited to learners in North America.
"Note: This course works best for learners who are based in the North America region."
No discussion on why accuracy is problematic and why we need to use metrics like F1.
"No discussion is made of why accuracy is problematical ... and why we need to use metrics like F1."
Deep neural network is very basic and only gets 73% accuracy.
"However the Deep neural network is very basic and only gets 73% accuracy."
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 Natural Language Processing for Stocks News Analysis with these
activities:
Find a mentor in the field of NLP
Show steps
Connect with a mentor to receive guidance and support in your NLP learning journey.
Show steps
-
Attend industry events and meetups.
-
Reach out to people in your network.
-
Use online platforms to find a mentor.
Review LSTM and deep learning models
Show steps
Start the course by reviewing the LSTM deep learning model. This will help you understand the course materials more easily.
Browse courses on
LSTM
Show steps
-
Read the course materials on LSTM deep learning models.
-
Watch videos on LSTM deep learning models.
-
Take a quiz on LSTM deep learning models.
NLP data preprocessing exercises
Show steps
Practice NLP data preprocessing to improve your understanding of the course materials.
Show steps
-
Find a dataset that you would like to use for your NLP project.
-
Preprocess the data by removing stop words, stemming words, and lemmatizing words.
-
Create a feature vector for each data point.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group
Show steps
Collaborate with peers and reinforce your learning through a study group.
Show steps
-
Find other students who are taking the same course.
-
Form a study group and meet regularly.
-
Discuss the course materials, complete assignments together, and quiz each other.
Volunteer at an NLP event
Show steps
Expand your network and learn from others by volunteering at an NLP event.
Show steps
-
Find an NLP event that you are interested in.
-
Contact the event organizers to inquire about volunteer opportunities.
-
Attend the event and volunteer your time.
Sentiment analysis project
Show steps
Build a sentiment analysis project to apply the skills you learn in the course.
Show steps
-
Choose a dataset for your project.
-
Build a model to train on the dataset.
-
Evaluate the performance of your model.
Write a blog post about your project
Show steps
Share your knowledge by writing a blog post about the project you built in the course.
Show steps
-
Choose a topic for your blog post.
-
Write the content for your blog post.
-
Publish your blog post.
Participate in a Kaggle competition
Show steps
Challenge yourself by participating in a Kaggle competition related to NLP.
Show steps
-
Find a competition that you are interested in.
-
Read the competition rules and data.
-
Build a model to compete in the competition.
Contribute to an open-source NLP project
Show steps
Gain practical experience by contributing to an open-source NLP project.
Show steps
-
Find an open-source NLP project that you are interested in.
-
Read the project documentation.
-
Make a contribution to the project.
Find a mentor in the field of NLP
Show steps
Connect with a mentor to receive guidance and support in your NLP learning journey.
Show steps
Show steps
Show steps
- Attend industry events and meetups.
- Reach out to people in your network.
- Use online platforms to find a mentor.
Review LSTM and deep learning models
Show steps
Start the course by reviewing the LSTM deep learning model. This will help you understand the course materials more easily.
Browse courses on
LSTM
Show steps
Show steps
Show steps
- Read the course materials on LSTM deep learning models.
- Watch videos on LSTM deep learning models.
- Take a quiz on LSTM deep learning models.
NLP data preprocessing exercises
Show steps
Practice NLP data preprocessing to improve your understanding of the course materials.
Show steps
Show steps
Show steps
- Find a dataset that you would like to use for your NLP project.
- Preprocess the data by removing stop words, stemming words, and lemmatizing words.
- Create a feature vector for each data point.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group
Show steps
Collaborate with peers and reinforce your learning through a study group.
Show steps
Show steps
Show steps
- Find other students who are taking the same course.
- Form a study group and meet regularly.
- Discuss the course materials, complete assignments together, and quiz each other.
Volunteer at an NLP event
Show steps
Expand your network and learn from others by volunteering at an NLP event.
Show steps
Show steps
Show steps
- Find an NLP event that you are interested in.
- Contact the event organizers to inquire about volunteer opportunities.
- Attend the event and volunteer your time.
Sentiment analysis project
Show steps
Build a sentiment analysis project to apply the skills you learn in the course.
Show steps
Show steps
Show steps
- Choose a dataset for your project.
- Build a model to train on the dataset.
- Evaluate the performance of your model.
Write a blog post about your project
Show steps
Share your knowledge by writing a blog post about the project you built in the course.
Show steps
Show steps
Show steps
- Choose a topic for your blog post.
- Write the content for your blog post.
- Publish your blog post.
Participate in a Kaggle competition
Show steps
Challenge yourself by participating in a Kaggle competition related to NLP.
Show steps
Show steps
Show steps
- Find a competition that you are interested in.
- Read the competition rules and data.
- Build a model to compete in the competition.
Contribute to an open-source NLP project
Show steps
Gain practical experience by contributing to an open-source NLP project.
Show steps
Show steps
Show steps
- Find an open-source NLP project that you are interested in.
- Read the project documentation.
- Make a contribution to the project.
Career center
Learners who complete Natural Language Processing for Stocks News Analysis will develop knowledge and skills
that may be useful to these careers:
NLP Engineer
NLP Engineer
NLP Engineers can enhance their knowledge and skills by taking this course, which provides a comprehensive introduction to NLP techniques for analyzing large datasets, including Twitter data. The hands-on project, which involves training an LSTM deep learning model for stock sentiment analysis, provides valuable experience in applying NLP to real-world problems.
Machine Learning Engineer
Machine Learning Engineer
The Machine Learning Engineer can gain hands-on experience by training an LSTM deep learning model using this course to perform stock sentiment analysis. Additionally, this course may be particularly relevant to Machine Learning Engineers interested in using NLP for financial data analysis
Data Scientist
Data Scientist
The LSTM deep learning model used in this course gives Data Scientists the opportunity to explore sophisticated deep learning models. Furthermore, the model can automatically analyze people’s sentiment toward companies and stocks by analyzing millions of tweets. Data Scientists may also leverage these skills to solve other types of unstructured data challenges in their everyday work.
Quantitative Analyst
Quantitative Analyst
The Quantitative Analyst role can benefit from this course, as it provides exposure to NLP techniques commonly used in financial analysis. Specifically, the course's focus on analyzing Twitter data to predict sentiment toward companies and stocks may prove valuable for Quantitative Analysts looking to enhance their skill set.
Investment Analyst
Investment Analyst
Investment Analysts can benefit from taking this course to gain knowledge of NLP techniques used in analyzing market sentiment and making investment decisions. The course's hands-on project involving training an LSTM deep learning model for stock sentiment analysis provides valuable experience in applying NLP to real-world financial data.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers can strengthen their skill set by taking this course. The course's focus on using NLP to analyze Twitter data and predict sentiment towards companies and stocks provides valuable exposure to NLP techniques and their applications in the financial industry.
Financial Analyst
Financial Analyst
Financial Analysts may find this course helpful in understanding how NLP techniques can be used to analyze market sentiment and make informed investment decisions. The course provides hands-on experience with analyzing Twitter data to predict sentiment towards companies and stocks, which can be valuable for staying ahead in the dynamic financial landscape.
Risk Analyst
Risk Analyst
Risk Analysts may find this course useful as it provides an introduction to NLP techniques for analyzing public sentiment. Understanding how to analyze Twitter data to predict sentiment towards companies and stocks can be valuable for identifying potential risks and making informed decisions.
Product Manager
Product Manager
Product Managers seeking to incorporate NLP into their products may find this course beneficial. The course provides an understanding of NLP techniques for analyzing public sentiment, which can inform product development and decision-making.
Research Analyst
Research Analyst
This course can be useful for Research Analysts as it provides an introduction to Natural Language Processing (NLP) and its applications in analyzing public sentiment towards companies and stocks. Research Analysts may find the hands-on experience with text analysis and sentiment analysis particularly relevant to their work.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts can gain experience in presenting NLP insights through data visualization. The course's hands-on project, which involves analyzing Twitter data and predicting sentiment, provides an opportunity to develop skills in visualizing NLP results for effective communication.
Business Analyst
Business Analyst
Business Analysts can gain exposure to NLP and its applications in the financial sector through this course. The course's focus on analyzing Twitter data to understand public sentiment towards companies and stocks can provide valuable insights for making informed business decisions.
Marketing Analyst
Marketing Analyst
Marketing Analysts can gain exposure to NLP and its applications in the marketing field through this course. The course's focus on analyzing Twitter data to understand public sentiment towards companies and stocks can provide valuable insights for developing effective marketing strategies.
Data Analyst
Data Analyst
The Data Analyst position thrives in this course through the chance to work with complex data sets relating to Natural Language Processing. Twitter posts are then used for training an AI/ML model in this project, providing hands-on experience with large datasets. This course builds a practical skill set necessary for success in this role.
Software Engineer
Software Engineer
Software Engineers can build a foundation for NLP applications by taking this course. The hands-on project involving training an LSTM deep learning model for stock sentiment analysis provides valuable experience in developing and deploying NLP solutions.
For more career information including salaries, visit:
OpenCourser.com/course/2by9xb/natural
Reading list
We've selected eight 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
Natural Language Processing for Stocks News Analysis.
Save
Provides comprehensive coverage of NLP techniques, including deep learning. It's a valuable resource for gaining a deeper understanding of the concepts and techniques used in the course.
Focuses specifically on deep learning techniques for NLP. It provides detailed explanations and practical examples of how to build and train deep learning models for sentiment analysis.
This classic textbook provides a comprehensive overview of NLP, including chapters on sentiment analysis and machine learning. It's a great resource for supplementary reading and background knowledge.
Provides a hands-on approach to NLP, with a focus on building practical applications. It includes chapters on sentiment analysis and machine learning, making it a valuable resource for the course.
Provides a practical guide to NLP with Python, covering a wide range of techniques and applications. It's a valuable resource for understanding the practical implementation of the concepts used in the course.
Provides a practical guide to NLP with Python, covering a wide range of techniques, including sentiment analysis and machine learning. It's a valuable resource for understanding the practical implementation of the concepts used in the course.
Provides a comprehensive overview of sentiment analysis and opinion mining techniques. It covers a wide range of topics, including machine learning and deep learning approaches.
Save
Provides a comprehensive overview of machine learning, covering a wide range of topics, including supervised and unsupervised learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2by9xb/natural
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Good to know
Emphasizes sentiment analysis, a crucial NLP technique used in many industries
Utilizes LSTM deep learning models, a leading technology in NLP
Provides hands-on experience through a practical project, allowing learners to apply their knowledge
Taught by industry expert Ryan Ahmed, who brings real-world insights
Focuses on stock sentiment analysis, which has significant applications in finance and investing
Note that course accessibility is currently limited to the North America region
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