University of California, Irvine
Natural Language Processing and Capstone Assignment
University of California, Irvine

Natural Language Processing and Capstone Assignment

This course is part of Data Science Fundamentals Specialization

Taught in English

Some content may not be translated

Julie Pai

Instructor: Julie Pai

2,703 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.6

(29 reviews)

Intermediate level
Some related experience required
4 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Applications of natural language processing

  • Basics of social media analytics

  • Future trends and possibilities in data science

Details to know

Shareable certificate

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Assessments

2 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(29 reviews)

Intermediate level
Some related experience required
4 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Science Fundamentals Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 4 modules in this course

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.

What's included

1 video2 readings1 discussion prompt

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.

What's included

2 readings1 quiz

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.

What's included

1 video2 readings1 discussion prompt

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.

What's included

2 readings1 quiz1 peer review

Instructor

Instructor ratings
3.7 (5 ratings)
Julie Pai
University of California, Irvine
8 Courses19,860 learners

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