Practical Natural Language Processing
Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana
Endorsed by Zachary Lipton, Sebastian Ruder & Marc Najork
Foreword by Julian McAuley
Read the testimonials
Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana
Endorsed by Zachary Lipton, Sebastian Ruder & Marc Najork
Foreword by Julian McAuley
Read the testimonials
Eberhard Karls University
Tübingen, Germany
NLP without Annotated Data was course taught at SfS, Uni-Tuebingen in January 2021. It focuses on applying natural language processing techniquies with limited and no data. These slides are uploaded "as is", and hence are not polished for public consumption.
Plaksha University
Punjab, India
This was a course taught to undergraduate and graduate engineering students. Slides will be uploaded soon.
Ludwig Maximilian University
Munich, Germany
This course introduces Natural Language Processing (and Machine Learning) to economist and explores how it can be used to address research questions in economics that require analysis of textual data.
National Taiwan Normal University
Taipei City, Taiwan
This course aims to introduce useful strategies and common workflows that have been widely adopted by data scientists to extract useful insights from the textual data.
University of Vienna
Vienna, Austria
This is a practice-oriented course that aims to help students master the use of linguistic resources and tools, and efficiently apply them to independently design and implement solutions for subject-specific problems
Marymount University
Arlington County, Virginia
This is a master's level course where students examine the complexities of language phenomena and how to handle those using current NLP tools and scripting techniques.
University of North Texas
Denton, Texas
The course covers a survey of problems, methods, and theory of computational linguistics and natural language processing, with a particular focus on linguistically-oriented approaches.
University of Campinas
São Paulo, Brazil
This course provides the knowledge to construct and use deep neural networks for image and text analysis. The course starts from the basic concepts to understand, train and test neural networks for classification and regression. It introduces image analysis. In the sequence, it provides an introduction to text analysis and then covers Recurrent Neural Networks, Attention, Transformers and applications in text analysis.