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).
This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Este curso faz parte do Programa de cursos integrados Applied Data Science with Python
Applied Text Mining in Python
oferecido por
Informações sobre o curso
4.2
1,302 classificações
O que você vai aprender
Apply basic natural language processing methods
Describe the nltk framework for manipulating text
Understand how text is handled in Python
Write code that groups documents by topic
Habilidades que você terá
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
Programa - O que você aprenderá com este curso
Semana
1Module 1: Working with Text in Python
5 videos (Total 56 min), 4 leituras, 3 testes
5 videos
Handling Text in Python18min
Regular Expressions16min
Demonstration: Regex with Pandas and Named Groups5min
Internationalization and Issues with Non-ASCII Characters12min
4 leituras
Course Syllabus10min
Help us learn more about you!10min
Notice for Auditing Learners: Assignment Submission10min
Resources: Common issues with free text10min
2 exercícios práticos
Practice Quiz8min
Module 1 Quiz12min
Semana
2Module 2: Basic Natural Language Processing
3 videos (Total 36 min), 3 testes
3 videos
Basic NLP tasks with NLTK16min
Advanced NLP tasks with NLTK16min
2 exercícios práticos
Practice Quiz4min
Module 2 Quiz10min
Semana
3Module 3: Classification of Text
7 videos (Total 94 min), 2 testes
7 videos
Text Classification12min
Identifying Features from Text8min
Naive Bayes Classifiers19min
Naive Bayes Variations4min
Support Vector Machines24min
Learning Text Classifiers in Python15min
Demonstration: Case Study - Sentiment Analysis9min
1 exercício prático
Module 3 Quiz14min
Semana
4Module 4: Topic Modeling
4 videos (Total 58 min), 2 leituras, 3 testes
4 videos
Topic Modeling8min
Generative Models and LDA13min
Information Extraction18min
2 leituras
Additional Resources & Readings10min
Post-Course Survey10min
2 exercícios práticos
Practice Quiz4min
Module 4 Quiz10min
20%
comecei uma nova carreira após concluir estes cursos
83%
consegui um benefício significativo de carreira com este curso
10%
recebi um aumento ou promoção
Melhores avaliações
por CC•Aug 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!
por CB•Sep 20th 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
Instrutores
V. G. Vinod Vydiswaran
Assistant ProfessorSchool of Information
Sobre University of Michigan
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.
Sobre o Programa de cursos integrados Applied Data Science with Python
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.
O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.
© 2018 Coursera Inc. Todos os direitos reservados.
