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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About this Course
최근 조회 147,119회
학습자 경력 결과
32%
이 강좌를 수료한 후 새로운 경력 시작하기
34%
이 강좌를 통해 확실한 경력상 이점 얻기
귀하가 습득할 기술
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
학습자 경력 결과
32%
이 강좌를 수료한 후 새로운 경력 시작하기
34%
이 강좌를 통해 확실한 경력상 이점 얻기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 17시간 필요
권장: 9 hours/week
영어
자막: 영어, 한국어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 8시간 필요
Module 1: Working with Text in Python
완료하는 데 8시간 필요
5개 동영상 (총 56분), 4 readings, 3 quizzes
5개의 동영상
Handling Text in Python18m
Regular Expressions16m
Demonstration: Regex with Pandas and Named Groups5m
Internationalization and Issues with Non-ASCII Characters12m
4개의 읽기 자료
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Resources: Common issues with free text10m
2개 연습문제
Practice Quiz8m
Module 1 Quiz12m
완료하는 데 6시간 필요
Module 2: Basic Natural Language Processing
완료하는 데 6시간 필요
3개 동영상 (총 36분)
3개의 동영상
Basic NLP tasks with NLTK16m
Advanced NLP tasks with NLTK16m
2개 연습문제
Practice Quiz4m
Module 2 Quiz10m
완료하는 데 7시간 필요
Module 3: Classification of Text
완료하는 데 7시간 필요
7개 동영상 (총 94분)
7개의 동영상
Identifying Features from Text8m
Naive Bayes Classifiers19m
Naive Bayes Variations4m
Support Vector Machines24m
Learning Text Classifiers in Python15m
Demonstration: Case Study - Sentiment Analysis9m
1개 연습문제
Module 3 Quiz14m
완료하는 데 6시간 필요
Module 4: Topic Modeling
완료하는 데 6시간 필요
4개 동영상 (총 58분), 2 readings, 3 quizzes
4개의 동영상
Topic Modeling8m
Generative Models and LDA13m
Information Extraction18m
2개의 읽기 자료
Additional Resources & Readings10m
Post-Course Survey10m
2개 연습문제
Practice Quiz4m
Module 4 Quiz10m
검토
4.2
APPLIED TEXT MINING IN PYTHON의 최상위 리뷰
RK 제공Jul 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.
JS 제공Aug 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).
LC 제공Feb 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.
GK 제공May 3, 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. :-)
BK 제공Jun 25, 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.
AG 제공Jan 12, 2019
It would help to connect the teaching better to the assignments. Especially in Week 4 there is to little connection. There is not even a course notebook to practice some of the material.
CB 제공Sep 19, 2017
Excellent course! Video lectures are high quality, with realistic problems and applications. Exercises are reasonably challenging, and all quite fun to do! Strongly recommend this course
FA 제공Jul 3, 2019
This course give the basic idea in each module existed in text and natural language processing kits. A lot more for self-explore, but this will intrigue to begin sooner and learn wider.
JL 제공Oct 25, 2017
The course itself is good, but the assigment system is not robust and some sentences are also ambiguous to users. Seeing from the forums, many users get confused in the assigments.
AS 제공Nov 29, 2019
Course is well explained with practice exercise. Only suggestion is that for assignment there is no way to find why a particular output is wrong. There should be some hint for it.
AM 제공Aug 1, 2019
Excellent course for someone like me who is ambitious and aspires to gain knowledge on new things. The videos can be made bit more elaborate, seems to be rushing towards the end.
MA 제공Sep 7, 2018
Just enough theory and an comprehensive guide through regex, nltk and some features from gensim (LDA). Assignmets are very challenging and some nice utilities are developed.
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미시건 대학교
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.
Python과 함께하는 응용 데이터 과학 전문 분야 정보
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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