Master text mining and NLP techniques using Python, from basic text processing to advanced topic modeling.
Master text mining and NLP techniques using Python, from basic text processing to advanced topic modeling.
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 Applied Data Science with Python 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.
4.2
(3,807 ratings)
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Instructors:
English
پښتو, বাংলা, اردو, 2 more
What you'll learn
Handle and manipulate text data in Python
Implement natural language processing techniques
Build text classification systems
Develop topic modeling solutions
Master text cleaning and preprocessing
Create practical text mining applications
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers text mining and natural language processing using Python. Starting with basic text handling and manipulation, students progress through natural language processing tasks, text classification, and topic modeling. The curriculum includes hands-on experience with NLTK framework, regular expressions, and advanced text analysis techniques. Students learn to apply machine learning methods for text classification and develop practical skills in processing and analyzing textual data.
Working with Text in Python
Module 1 · 7 Hours to complete
Basic Natural Language Processing
Module 2 · 5 Hours to complete
Classification of Text
Module 3 · 6 Hours to complete
Topic Modeling
Module 4 · 5 Hours to complete
Fee Structure
Instructor
Innovator in Health Informatics and Natural Language Processing
Dr. V. G. Vinod Vydiswaran is an Assistant Professor at the University of Michigan, holding positions in both the Medical School's Department of Learning Health Sciences and the School of Information. His research focuses on critical areas such as information trustworthiness, large-scale text mining, natural language processing (NLP), and machine learning. Dr. Vydiswaran's current work involves mining and analyzing health information from diverse sources, including scientific literature, community health forums, and social networks, with a particular emphasis on assessing the credibility of online medical information and its implications for healthcare.In addition to his research endeavors, Dr. Vydiswaran teaches courses on applied text mining and information extraction in health contexts, equipping students with essential skills to navigate and analyze health data effectively. His innovative applications of algorithmic models aim to tackle real-world challenges in healthcare, making significant contributions to the field of health informatics and enhancing the understanding of how information impacts patient care and health outcomes
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4.2 course rating
3,807 ratings
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