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Julie Pai
Welcome to Natural Language Processing and Capstone Assignment. In this course we will begin with an Recognize how technical and business techniques can be used to deliver business insight, competitive intelligence, and consumer sentiment. The course concludes with a capstone assignment in which you will apply a wide range of what has been covered in this specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/825tyr/natural
What's inside
Syllabus
Natural Language Processing I
Welcome to Module 1, Natural Language Processing I. In this module we will begin with an introduction to text analytics, or natural language processing (NLP). We will explore the numerous applications of NLP and discuss one of the most popular applications - sentiment analysis.
Read more
Syllabus
Natural Language Processing I
Welcome to Module 1, Natural Language Processing I. In this module we will begin with an introduction to text analytics, or natural language processing (NLP). We will explore the numerous applications of NLP and discuss one of the most popular applications - sentiment analysis.
Read more
Natural Language Processing II
Welcome to Module 2, Natural Language Processing II. In this module we will continue our exploration of natural language processing with a review of topic modeling and one of the most effective topic detection techniques currently in use - Latent Dirichlet allocation (LDA). In addition, we will define several technical terms and concepts commonly used in text mining.
The Past, Present, and Future of Data Science I
Welcome to Module 3, Past, Present, and Future of Data Science I. In this module we will provide a historical perspective of the terminology applied to data analytics, as well as a forward-looking discussion of several key trends emerging in data science. We will also explore several leading-edge enablers and enhancers of data science, including deep learning, explainable AI, and automated machine learning.
The Past, Present, and Future of Data Science II
Welcome to Module 4, Past, Present, and Future of Data Science II. In this module we will continue our exploration of new practices in data science and predictive modelling, including model ensembles, sensor technologies and IoT, geospatial analytics, and cloud computing. We will conclude this program with an activity to bring everything you’ve learned in this program together to develop a data analytics plan.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines the variety of applications of NLP and one of the most popular applications, sentiment analysis
Introduces text analytics, or natural language processing (NLP), to learners
Builds upon existing NLP knowledge with training on topic modeling and Latent Dirichlet allocation (LDA)
Uses a capstone assignment as a culminating application of learned techniques
Provides a historical perspective and review of leading-edge enablers of data science and predictive modeling
Requires extensive background knowledge in data science first
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Save Natural Language Processing and Capstone Assignment to your list so you can find it easily later:
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Reviews summary
Capstone course with mixed reviews
Students have largely positive feelings towards this course, though some report missing hands-on practice. They report that the course provides a good overview of the uses of natural language processing, particularly for those with non-technical backgrounds.
Provides a broad understanding of NLP for non-programmers.
"A good overview of the uses of natural language processing for a non-technical background."
"Very nice course!!"
"Thank you so much"
No hands-on programming practice.
"No programming practice"
"It is literally just a few descriptions of concepts you can get wiki, lacks substance."
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 and Capstone Assignment with these
activities:
Review basic programming concepts
Show steps
Strengthen your comprehension of programming fundamentals to enhance your understanding of NLP techniques.
Show steps
-
Revisit core programming concepts such as data structures, algorithms, and object-oriented programming.
-
Complete coding exercises and practice implementing basic programming constructs.
Attend conferences or meetups on NLP
Show steps
Connect with professionals in the field of NLP by attending conferences or meetups to learn from others and expand your network.
Browse courses on
Natural Language Processing
Show steps
-
Identify upcoming NLP conferences or meetups in your area.
-
Register for the event and make an effort to attend.
-
Engage in discussions, ask questions, and share your perspectives.
-
Exchange contact information with other attendees to build your professional network.
Join an NLP study group
Show steps
Enhance your learning through collaboration by joining an NLP study group.
Browse courses on
Natural Language Processing
Show steps
-
Find or create an NLP study group with peers who share similar learning goals.
-
Establish meeting times and agendas for the study group.
-
Take turns leading discussions and presenting on different NLP topics.
-
Work together to solve problems and complete assignments.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on NLP techniques
Show steps
Supplement your learning by following guided tutorials to further refine or develop your NLP skills.
Browse courses on
Natural Language Processing
Show steps
-
Identify reputable sources for NLP tutorials.
-
Select tutorials that align with your learning goals and experience level.
-
Follow the instructions and complete the exercises in the tutorials.
-
Experiment with different NLP techniques and apply them to practical examples.
Build an NLP project
Show steps
Build a project in NLP to test your skills, improve your knowledge and understanding
Browse courses on
Natural Language Processing
Show steps
-
Identify a problem or challenge that can be addressed using NLP.
-
Gather and prepare the necessary data for your NLP project.
-
Choose and apply appropriate NLP techniques to analyze and process the data.
-
Evaluate the performance of your NLP project and make necessary adjustments.
-
Present and share the outcomes of your NLP project with others.
Develop a blog post on NLP
Show steps
Enhance your understanding by creating a blog post on NLP, which will help you solidify your learning and share your knowledge with others.
Browse courses on
Natural Language Processing
Show steps
-
Choose a specific topic in NLP that you want to write about.
-
Research and gather information from credible sources.
-
Organize your thoughts and structure your blog post logically.
-
Write clear and engaging content, explaining the NLP concepts and techniques.
-
Publish your blog post and promote it on relevant platforms.
Compile a list of NLP resources
Show steps
Organize and expand your understanding of NLP by compiling a comprehensive list of resources such as articles, tutorials, and tools.
Browse courses on
Natural Language Processing
Show steps
-
Identify and gather relevant NLP resources from various sources.
-
Organize the resources into categories or topics.
-
Annotate the resources with brief descriptions and your own insights.
Mentor junior NLP enthusiasts
Show steps
Solidify your understanding by mentoring junior NLP enthusiasts, helping them to grasp concepts and develop their skills.
Browse courses on
Natural Language Processing
Show steps
-
Identify opportunities to mentor junior learners or newcomers to the field of NLP.
-
Share your knowledge and expertise to guide and support their learning journey.
-
Provide constructive feedback and encouragement to help them overcome challenges.
Review basic programming concepts
Show steps
Strengthen your comprehension of programming fundamentals to enhance your understanding of NLP techniques.
Show steps
Show steps
Show steps
- Revisit core programming concepts such as data structures, algorithms, and object-oriented programming.
- Complete coding exercises and practice implementing basic programming constructs.
Attend conferences or meetups on NLP
Show steps
Connect with professionals in the field of NLP by attending conferences or meetups to learn from others and expand your network.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify upcoming NLP conferences or meetups in your area.
- Register for the event and make an effort to attend.
- Engage in discussions, ask questions, and share your perspectives.
- Exchange contact information with other attendees to build your professional network.
Join an NLP study group
Show steps
Enhance your learning through collaboration by joining an NLP study group.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Find or create an NLP study group with peers who share similar learning goals.
- Establish meeting times and agendas for the study group.
- Take turns leading discussions and presenting on different NLP topics.
- Work together to solve problems and complete assignments.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on NLP techniques
Show steps
Supplement your learning by following guided tutorials to further refine or develop your NLP skills.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify reputable sources for NLP tutorials.
- Select tutorials that align with your learning goals and experience level.
- Follow the instructions and complete the exercises in the tutorials.
- Experiment with different NLP techniques and apply them to practical examples.
Build an NLP project
Show steps
Build a project in NLP to test your skills, improve your knowledge and understanding
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify a problem or challenge that can be addressed using NLP.
- Gather and prepare the necessary data for your NLP project.
- Choose and apply appropriate NLP techniques to analyze and process the data.
- Evaluate the performance of your NLP project and make necessary adjustments.
- Present and share the outcomes of your NLP project with others.
Develop a blog post on NLP
Show steps
Enhance your understanding by creating a blog post on NLP, which will help you solidify your learning and share your knowledge with others.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Choose a specific topic in NLP that you want to write about.
- Research and gather information from credible sources.
- Organize your thoughts and structure your blog post logically.
- Write clear and engaging content, explaining the NLP concepts and techniques.
- Publish your blog post and promote it on relevant platforms.
Compile a list of NLP resources
Show steps
Organize and expand your understanding of NLP by compiling a comprehensive list of resources such as articles, tutorials, and tools.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify and gather relevant NLP resources from various sources.
- Organize the resources into categories or topics.
- Annotate the resources with brief descriptions and your own insights.
Mentor junior NLP enthusiasts
Show steps
Solidify your understanding by mentoring junior NLP enthusiasts, helping them to grasp concepts and develop their skills.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners or newcomers to the field of NLP.
- Share your knowledge and expertise to guide and support their learning journey.
- Provide constructive feedback and encouragement to help them overcome challenges.
Career center
Learners who complete Natural Language Processing and Capstone Assignment will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist combines programming skills with the ability to analyze and interpret data to help organizations make better decisions. Data Scientists are often employed by large companies that need to leverage large volumes of data to improve efficiency and productivity. This course may be useful in teaching essential skills for Data Scientists, such as natural language processing and text analytics. These skills are essential for extracting meaningful insights from unstructured data, which is often a major challenge for Data Scientists.
Data Analyst
Data Analyst
A Data Analyst works in a business or organizational context to analyze and interpret data, often to help make better decisions. Data Analysts are often responsible for gathering, cleaning, and analyzing data, and then communicating their findings to stakeholders. This course may be useful in teaching essential skills for Data Analysts, such as natural language processing and text analytics. These skills are essential for extracting meaningful insights from unstructured data, which is often a major challenge for Data Analysts.
Customer Experience Analyst
Customer Experience Analyst
A Customer Experience Analyst works with customers to understand their needs and then develop solutions to meet those needs. Customer Experience Analysts often use data analysis to help make better decisions. This course may be useful in teaching essential skills for Customer Experience Analysts, such as natural language processing and text analytics. These skills are essential for understanding customer feedback and sentiment, which is often a major challenge for Customer Experience Analysts.
Business Analyst
Business Analyst
A Business Analyst works with business stakeholders to understand their needs and then develop solutions to meet those needs. Business Analysts often use data analysis to help make better decisions. This course may be useful in teaching essential skills for Business Analysts, such as natural language processing and text analytics. These skills are essential for understanding customer feedback and sentiment, which is often a major challenge for Business Analysts.
Operations Analyst
Operations Analyst
An Operations Analyst works with operations teams to understand business processes and then develop solutions to improve efficiency and productivity. Operations Analysts often use data analysis to help make better decisions. This course may be useful in teaching essential skills for Operations Analysts, such as natural language processing and text analytics. These skills are essential for understanding customer feedback and sentiment, which is often a major challenge for Operations Analysts.
User Experience Analyst
User Experience Analyst
A User Experience Analyst works with product teams to understand user needs and then develop products that meet those needs. User Experience Analysts often use data analysis to help make better decisions. This course may be useful in teaching essential skills for User Experience Analysts, such as natural language processing and text analytics. These skills are essential for understanding user feedback and sentiment, which is often a major challenge for User Experience Analysts.
Market Analyst
Market Analyst
A Market Analyst researches market trends and conditions to help organizations make better decisions. Market Analysts often use data analysis to help make better decisions. This course may be useful in teaching essential skills for Market Analysts, such as natural language processing and text analytics. These skills are essential for understanding customer feedback and sentiment, which is often a major challenge for Market Analysts.
Product Analyst
Product Analyst
A Product Analyst works with product teams to understand customer needs and then develop products that meet those needs. Product Analysts often use data analysis to help make better decisions. This course may be useful in teaching essential skills for Product Analysts, such as natural language processing and text analytics. These skills are essential for understanding customer feedback and sentiment, which is often a major challenge for Product Analysts.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation to help users understand and use products and services. Technical Writers often use natural language processing and text analytics to help develop technical documentation that is clear and concise. This course may be useful in teaching essential skills for Technical Writers, such as natural language processing and text analytics. These skills are essential for building and maintaining technical documentation that is clear and concise.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer builds and deploys artificial intelligence models to help organizations solve problems. Artificial Intelligence Engineers often use natural language processing and text analytics to help develop artificial intelligence models that can understand and process human language. This course may be useful in teaching essential skills for Artificial Intelligence Engineers, such as natural language processing and text analytics. These skills are essential for building and deploying artificial intelligence models that can understand and process human language.
Research Analyst
Research Analyst
A Research Analyst researches market trends and conditions to help organizations make better decisions. Research Analysts often use natural language processing and text analytics to help develop research reports that can understand and process human language. This course may be useful in teaching essential skills for Research Analysts, such as natural language processing and text analytics. These skills are essential for building and deploying research reports that can understand and process human language.
Software Engineer
Software Engineer
A Software Engineer builds and maintains software systems to help organizations solve problems. Software Engineers often use natural language processing and text analytics to help develop software systems that can understand and process human language. This course may be useful in teaching essential skills for Software Engineers, such as natural language processing and text analytics. These skills are essential for building and maintaining software systems that can understand and process human language.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models to help organizations solve problems. Machine Learning Engineers often use natural language processing and text analytics to help develop machine learning models that can understand and process human language. This course may be useful in teaching essential skills for Machine Learning Engineers, such as natural language processing and text analytics. These skills are essential for building and deploying machine learning models that can understand and process human language.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data systems to help organizations store and process data. Data Engineers often use natural language processing and text analytics to help develop data systems that can understand and process human language. This course may be useful in teaching essential skills for Data Engineers, such as natural language processing and text analytics. These skills are essential for building and maintaining data systems that can understand and process human language.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to help organizations make better decisions. Quantitative Analysts often use natural language processing and text analytics to help develop models that can understand and process human language. This course may be useful in teaching essential skills for Quantitative Analysts, such as natural language processing and text analytics. These skills are essential for building and deploying models that can understand and process human language.
For more career information including salaries, visit:
OpenCourser.com/course/825tyr/natural
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
Natural Language Processing and Capstone Assignment.
Provides a practical introduction to text analytics with Python, covering topics like text preprocessing, text mining, and sentiment analysis. It valuable resource for learners who want to gain hands-on experience with text analytics.
Provides a comprehensive overview of NLP, covering topics like text preprocessing, text classification, and machine translation. It valuable resource for learners who want to gain a broad understanding of NLP.
The book provides a comprehensive introduction to NLP with Python, covering topics like text preprocessing, text classification, and machine translation. It valuable resource for learners who want to gain practical NLP skills.
Provides a comprehensive overview of speech and language processing, covering topics like speech recognition, natural language understanding, and dialogue systems. It valuable resource for learners who want to gain a broad understanding of speech and language processing.
Provides a practical introduction to data science for business, covering topics like data analysis, data mining, and machine learning. It valuable resource for learners who want to gain hands-on experience with data science for business.
Save
Provides a comprehensive overview of deep learning, covering topics like neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for learners who want to gain a broad understanding of deep learning.
Provides a comprehensive overview of reinforcement learning, covering topics like Markov decision processes, value functions, and policy gradients. It valuable resource for learners who want to gain a broad understanding of reinforcement learning.
Provides a comprehensive overview of convex optimization, covering topics like linear programming, quadratic programming, and semidefinite programming. It valuable resource for learners who want to gain a broad understanding of convex optimization.
Save
Provides a comprehensive overview of natural language understanding, covering topics like text comprehension, natural language generation, and dialogue systems. It valuable resource for learners who want to gain a broad understanding of natural language understanding.
Provides a comprehensive overview of machine learning for natural language processing, covering topics like text classification, text clustering, and machine translation. It valuable resource for learners who want to gain a broad understanding of machine learning for natural language processing.
Provides a theoretical foundation for NLP, covering topics like hidden Markov models, language models, and machine translation. It useful reference for learners who want to understand the mathematical underpinnings of NLP.
Provides a comprehensive introduction to deep learning for NLP, covering topics like neural networks, attention mechanisms, and transformer models. It valuable resource for learners who want to gain expertise in deep learning for NLP.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/825tyr/natural
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Level
Intermediate
Via
Coursera
Institution
University of California, Irvine
Instructor
Julie Pai
Language
English
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This course belongs to a
collection
called
Data Science Fundamentals. It's comprised of four courses:
- Predictive Modeling, Model Fitting, and Regression Analysis
- Natural Language Processing and Capstone Assignment (viewing)
- Intro to Analytic Thinking, Data Science, and Data Mining
- Cluster Analysis, Association Mining, and Model Evaluation
Good to know
Examines the variety of applications of NLP and one of the most popular applications, sentiment analysis
Introduces text analytics, or natural language processing (NLP), to learners
Builds upon existing NLP knowledge with training on topic modeling and Latent Dirichlet allocation (LDA)
Uses a capstone assignment as a culminating application of learned techniques
Provides a historical perspective and review of leading-edge enablers of data science and predictive modeling
Requires extensive background knowledge in data science first
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