課程信息
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第 4 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為17 小時

建議:9 hours/week...

英語(English)

字幕:英語(English), 韓語

您將學到的內容有

  • 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

您將獲得的技能

Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing

第 4 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為17 小時

建議:9 hours/week...

英語(English)

字幕:英語(English), 韓語

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 8 小時

Module 1: Working with Text in Python

5 個視頻 (總計 56 分鐘), 4 個閱讀材料, 3 個測驗
5 個視頻
Handling Text in Python18分鐘
Regular Expressions16分鐘
Demonstration: Regex with Pandas and Named Groups5分鐘
Internationalization and Issues with Non-ASCII Characters12分鐘
4 個閱讀材料
Course Syllabus10分鐘
Help us learn more about you!10分鐘
Notice for Auditing Learners: Assignment Submission10分鐘
Resources: Common issues with free text10分鐘
2 個練習
Practice Quiz8分鐘
Module 1 Quiz12分鐘
2
完成時間為 6 小時

Module 2: Basic Natural Language Processing

3 個視頻 (總計 36 分鐘), 3 個測驗
3 個視頻
Basic NLP tasks with NLTK16分鐘
Advanced NLP tasks with NLTK16分鐘
2 個練習
Practice Quiz4分鐘
Module 2 Quiz10分鐘
3
完成時間為 7 小時

Module 3: Classification of Text

7 個視頻 (總計 94 分鐘), 2 個測驗
7 個視頻
Identifying Features from Text8分鐘
Naive Bayes Classifiers19分鐘
Naive Bayes Variations4分鐘
Support Vector Machines24分鐘
Learning Text Classifiers in Python15分鐘
Demonstration: Case Study - Sentiment Analysis9分鐘
1 個練習
Module 3 Quiz14分鐘
4
完成時間為 6 小時

Module 4: Topic Modeling

4 個視頻 (總計 58 分鐘), 2 個閱讀材料, 3 個測驗
4 個視頻
Topic Modeling8分鐘
Generative Models and LDA13分鐘
Information Extraction18分鐘
2 個閱讀材料
Additional Resources & Readings10分鐘
Post-Course Survey10分鐘
2 個練習
Practice Quiz4分鐘
Module 4 Quiz10分鐘
4.2
409 條評論Chevron Right

32%

完成這些課程後已開始新的職業生涯

34%

通過此課程獲得實實在在的工作福利

來自Applied Text Mining in Python的熱門評論

創建者 CCAug 27th 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!

創建者 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. :-)

講師

Avatar

V. G. Vinod Vydiswaran

Assistant Professor
School of Information

關於 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....

關於 借助 Python 应用数据科学 專項課程

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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