This course provides comprehensive knowledge for extracting, cleaning, and preparing data for NLP applications. It requires basic to intermediate programming skills, preferably in Python, and familiarity with the Anaconda Jupyter Notebooks environment. Python 3.6 or higher is used for application development. The course utilizes Anaconda's Notebook as the primary code editor but allows the use of any text editor supporting Anaconda notebooks. Essential libraries include NLTK, Pandas, Scikit-learn, and data extraction libraries.
The course comprises four modules:
Each module explores specific topics such as web scraping, HTML parsing, advanced scraping, and text manipulation. The content includes lectures, tests, and supplementary materials to ensure a comprehensive learning experience.
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This course encompasses modules on web scraping, HTML parsing, advanced scraping techniques, and text manipulation, providing a comprehensive understanding of data preparation for NLP.
This module delves into web scraping for NLP, covering its stages, problem-solving capabilities, and techniques for extracting various web page formats. The content also includes a test, evaluation, and a podcast sharing professional experiences.
Explore HTML parsing techniques, including parser construction, parsing types, and in-depth parsing processes. The module also addresses parsing techniques, evaluation, and professional insights through a podcast.
This module focuses on advanced parsing, concurrent and distributed programming, programming advanced parsers, and selecting the appropriate programming language for parser construction. It also includes a test, evaluation, and a professional podcast.
Discover techniques for text manipulation, including connector construction, web scraping connectors, alternatives in connector construction, and modular connector construction. The module features tests, evaluations, and professional insights.
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