Natural Language Processing Fundamentals
Master the foundations of NLP and learn how computers process human language. Build practical text classification and suggestion systems using Python.
Master the foundations of NLP and learn how computers process human language. Build practical text classification and suggestion systems using Python.
This comprehensive course explores the fundamentals of Natural Language Processing (NLP), teaching students how computers understand, process, and generate human language. Led by experienced instructors with over 30 years of NLP expertise, the course covers essential concepts from text processing to language models. Students learn both traditional approaches and modern techniques, gaining hands-on experience through practical assignments including building a text classification application and a generative text suggestion system. The course combines theoretical knowledge with real-world applications, preparing students for advanced NLP concepts.
Instructors:
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What you'll learn
Understand the fundamental challenges and concepts of Natural Language Processing
Master text processing techniques and language representation methods
Develop proficiency in building and using language models
Create practical text classification applications using multiple paradigms
Implement regular expressions and minimum edit distance algorithms
Apply logistic regression and naive Bayes for text classification
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This foundational course in Natural Language Processing covers the essential concepts and techniques used in modern NLP applications. Students learn about text processing, language representation, language models, and text classification through a combination of theoretical instruction and practical assignments. The curriculum is structured to provide a solid understanding of both traditional NLP approaches and modern deep learning techniques. The course emphasizes hands-on learning through practical projects and is taught by experienced instructors with extensive research and teaching backgrounds in NLP.
What is NLP?
Module 1
Words
Module 2
Language Models
Module 3
Text Classification
Module 4
Fee Structure
Instructors
Distinguished NLP Scholar and Digital Libraries Pioneer
Min-Yen Kan serves as Vice Dean of Undergraduate Studies and Associate Professor at the National University of Singapore, bringing extensive expertise in natural language processing, digital libraries, and information retrieval. His academic credentials include BS, MS, and Ph.D. degrees from Columbia University, where he was part of the Natural Language Processing Group. As a senior member of both ACM and IEEE, he has made significant contributions to computational linguistics, including serving as the ACL Anthology Director (2008-2018) and currently co-chairing the ACL Ethics Committee. His research impact is evidenced by over 16,000 citations, with groundbreaking work in areas such as matrix factorization for online recommendations, keyphrase extraction, and scholarly paper recommendation systems. He leads the Web Information Retrieval/Natural Language Processing Group (WING) at NUS, where he has mentored over 150 research projects and taught more than 2,000 students. His excellence in teaching was recognized with an Excellent Teaching award in 2015, and he has played crucial roles in organizing major conferences in NLP, information retrieval, and digital libraries.
Social Media Analytics Expert and NUS Computing Lecturer
Christian von der Weth currently serves as a Senior Lecturer and Assistant Dean of Communications in the School of Computing at the National University of Singapore, bringing extensive expertise in social media analytics and artificial intelligence. His academic credentials include a Ph.D. from the Karlsruhe Institute of Technology and an M.Sc. from Ilmenau University of Technology in Germany. Before his current role, he gained valuable experience as a Senior Research Fellow at the NUS Centre for Research in Privacy Technologies (N-CRiPT) and as a Research Fellow at both Nanyang Technological University and the National University of Ireland, Galway. His research focuses on critical areas including social media and social network analysis, applied machine learning, natural language processing, and information systems security. His notable work includes developing strategies to combat algorithmic threats on social media and creating solutions to slow the spread of misinformation online. His publications in prestigious venues like ACM Multimedia and IEEE ICME demonstrate his significant contributions to understanding and addressing challenges in social media platforms
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