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).
提供方
课程信息
285,330 次近期查看
学生职业成果
32%
完成这些课程后已开始新的职业生涯
34%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 4 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为26 小时
英语（English）
字幕：英语（English）, 韩语
您将学到的内容有
Understand how text is handled in Python
Apply basic natural language processing methods
Write code that groups documents by topic
Describe the nltk framework for manipulating text
您将获得的技能
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
学生职业成果
32%
完成这些课程后已开始新的职业生涯
34%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 4 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为26 小时
英语（English）
字幕：英语（English）, 韩语
提供方
密歇根大学
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
教学大纲 - 您将从这门课程中学到什么
完成时间为 8 小时
Module 1: Working with Text in Python
完成时间为 8 小时
5 个视频 （总计 56 分钟）, 4 个阅读材料, 3 个测验
5 个视频
Handling Text in Python18分钟
Regular Expressions16分钟
Demonstration: Regex with Pandas and Named Groups5分钟
Internationalization and Issues with Non-ASCII Characters12分钟
4 个阅读材料
Course Syllabus10分钟
Help us learn more about you!10分钟
Notice for Auditing Learners: Assignment Submission10分钟
Resources: Common issues with free text10分钟
2 个练习
Practice Quiz8分钟
Module 1 Quiz12分钟
完成时间为 6 小时
Module 2: Basic Natural Language Processing
完成时间为 6 小时
3 个视频 （总计 36 分钟）
3 个视频
Basic NLP tasks with NLTK16分钟
Advanced NLP tasks with NLTK16分钟
2 个练习
Practice Quiz4分钟
Module 2 Quiz10分钟
完成时间为 7 小时
Module 3: Classification of Text
完成时间为 7 小时
7 个视频 （总计 94 分钟）
7 个视频
Identifying Features from Text8分钟
Naive Bayes Classifiers19分钟
Naive Bayes Variations4分钟
Support Vector Machines24分钟
Learning Text Classifiers in Python15分钟
Demonstration: Case Study - Sentiment Analysis9分钟
1 个练习
Module 3 Quiz14分钟
完成时间为 6 小时
Module 4: Topic Modeling
完成时间为 6 小时
4 个视频 （总计 58 分钟）, 2 个阅读材料, 3 个测验
4 个视频
Topic Modeling8分钟
Generative Models and LDA13分钟
Information Extraction18分钟
2 个阅读材料
Additional Resources & Readings10分钟
Post-Course Survey10分钟
2 个练习
Practice Quiz4分钟
Module 4 Quiz10分钟
审阅
来自APPLIED TEXT MINING IN PYTHON的热门评论
由 RK 提供Jul 19, 2019
Course is great except for the auto grader issues. Please look into the issue. I would like to take this opportunity and thank Prof V. G. Vinod Vydiswaran and all those who helped me to complete it.
由 CC 提供Aug 26, 2017
Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!
由 JS 提供Aug 22, 2017
Very good course with very good material and teachers. I just missed some more practical examples to follow along the classes, and more further readings (specially for information extraction).
由 LC 提供Feb 16, 2018
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.
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.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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