Natural Language Processing and Capstone Assignment
Master text analytics and NLP techniques while exploring trends in data science through hands-on projects in this intermediate-level course.
Master text analytics and NLP techniques while exploring trends in data science through hands-on projects in this intermediate-level course.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Fundamentals Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
(33 ratings)
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English
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What you'll learn
Understand applications of natural language processing
Master sentiment analysis and topic modeling techniques
Explore advanced data science trends and technologies
Apply text analytics to social media data
Develop comprehensive data analytics plans
Skills you'll gain
This course includes:
0.1 Hours PreRecorded video
2 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores natural language processing and advanced data science concepts. Students learn text analytics fundamentals, sentiment analysis, topic modeling using Latent Dirichlet allocation, and emerging trends in data science including deep learning, explainable AI, and automated machine learning. The course concludes with a capstone assignment integrating various data science techniques.
Natural Language Processing I
Module 1 · 53 Minutes to complete
Natural Language Processing II
Module 2 · 55 Minutes to complete
The Past, Present, and Future of Data Science I
Module 3 · 48 Minutes to complete
The Past, Present, and Future of Data Science II
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Assistant Director of Technology Programs
Julie Pai is an accomplished professional with over 10 years of experience in technology education programs. Her career began with a strong foundation in biology and clinical research, which ignited her interest in statistical analysis and data analysis. As the Assistant Director of Technology Programs at the University of California, Irvine, she collaborates with industry experts to design, launch, and manage a variety of courses in the technology sector, including Data Science, Data Analytics, Cloud Computing, and Machine Learning.Her technical expertise encompasses a range of tools and programming languages such as Tableau, SQL, MySQL, Python, Spark, Hive, and Scala. Julie is dedicated to enhancing educational offerings in technology and has developed courses that cover essential topics like predictive modeling and natural language processing. Her commitment to bridging the gap between academia and industry ensures that learners receive relevant and practical training in today's fast-evolving tech landscape.
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4.4 course rating
33 ratings
Frequently asked questions
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