This course will introduce the learner to text mining and text manipulation basics. The course 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 modelling).
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- 5 stars55.08%
- 4 stars25.24%
- 3 stars11.99%
- 2 stars4.34%
- 1 star3.33%
来自APPLIED TEXT MINING IN PYTHON的热门评论
Passionate instructor and a great primer on how software can infer useful data from text. Gives a preliminary understanding on the algorithms used in scikit learn and nltk.
Love the focus on conceptual text processing and practical guides to implementation in python, but the assignment grader was extremely specific for no reason, especially the Week3 assignment.
It is a great course with challenging assignments, I wish the syllabus is a little more deeper specially on the LDA part. But overall a good course that one can look for!
Excellent course to get started with text mining and NLP with Python. The course goes over the most essential elements involved with dealing with free text. Definitely worth the time I spent on it.
关于 借助 Python 应用数据科学 专项课程
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.