课程信息
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第 4 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

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17 小时

建议:9 hours/week...

英语(English)

字幕:英语(English), 韩语

您将学到的内容有

  • Check

    Apply basic natural language processing methods

  • Check

    Describe the nltk framework for manipulating text

  • Check

    Understand how text is handled in Python

  • Check

    Write code that groups documents by topic

您将获得的技能

Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing

第 4 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

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17 小时

建议:9 hours/week...

英语(English)

字幕:英语(English), 韩语

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 8 小时

Module 1: Working with Text in Python

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
2
完成时间为 6 小时

Module 2: Basic Natural Language Processing

3 个视频 (总计 36 分钟), 3 个测验
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
3
完成时间为 7 小时

Module 3: Classification of Text

7 个视频 (总计 94 分钟), 2 个测验
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
4
完成时间为 6 小时

Module 4: Topic Modeling

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
4.2
383 个审阅Chevron Right

31%

完成这些课程后已开始新的职业生涯

33%

通过此课程获得实实在在的工作福利

来自Applied Text Mining in Python的热门评论

创建者 GKMay 4th 2019

Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)

创建者 BKJun 26th 2018

Would love to see these courses have more practice questions in each weeks lesson. Would be helpful for repetition sake, and learning vs only doing each question once in the assignments.

讲师

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V. G. Vinod Vydiswaran

Assistant Professor
School of Information

关于 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....

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....
借助 Python 应用数据科学

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