Applied Text Mining in Python
Applied Text Mining in Python
Course 4 of 5 in the Applied Data Science with Python Specialization.
Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Institution: University of Michigan, via Coursera
Level: Intermediate
What will you learn in Applied text mining in Python?
The Applied text mining in Python from Coursera begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.
The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modeling).
This Applied Text Mining in Python course is part of “Applied Data Science with Python“ and is intended for learners who have a basic python or programming background and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data and/or a basic nltk tutorial.
Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. Learners with formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
