Natural Language Processing and Capstone Assignment

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Natural Language Processing and Capstone Assignment

This course is part of Data Science Fundamentals Specialization

Instructor: Julie Pai

3,929 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

50 reviews

Intermediate level

Some related experience required

5 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

50 reviews

Intermediate level

Some related experience required

5 hours to complete

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

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Data Science Fundamentals Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

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.

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 assignment

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 assignment1 peer review

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Instructor

Instructor ratings

(11 ratings)

Julie Pai

5 Courses20,019 learners

Offered by

University of California, Irvine

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