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 forma parte de Programa Especializado - Ciencias de los Datos Aplicada con Python
Applied Text Mining in Python
ofrecido por
Acerca de este Curso
4.2
1,617 calificaciones
Qué aprenderás
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 obtendrás
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
Programa - Qué aprenderás en este curso
Semana
18 horas para completar
Module 1: Working with Text in Python
5 videos (Total 56 minutos), 4 readings, 3 quizzes
5 videos
Handling Text in Python18m
Regular Expressions16m
Demonstration: Regex with Pandas and Named Groups5m
Internationalization and Issues with Non-ASCII Characters12m
4 lecturas
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Resources: Common issues with free text10m
2 ejercicios de práctica
Practice Quiz8m
Module 1 Quiz12m
Semana
26 horas para completar
Module 2: Basic Natural Language Processing
3 videos (Total 36 minutos), 3 quizzes
3 videos
Basic NLP tasks with NLTK16m
Advanced NLP tasks with NLTK16m
2 ejercicios de práctica
Practice Quiz4m
Module 2 Quiz10m
Semana
37 horas para completar
Module 3: Classification of Text
7 videos (Total 94 minutos), 2 quizzes
7 videos
Identifying Features from Text8m
Naive Bayes Classifiers19m
Naive Bayes Variations4m
Support Vector Machines24m
Learning Text Classifiers in Python15m
Demonstration: Case Study - Sentiment Analysis9m
1 ejercicio de práctica
Module 3 Quiz14m
Semana
46 horas para completar
Module 4: Topic Modeling
4 videos (Total 58 minutos), 2 readings, 3 quizzes
4 videos
Topic Modeling8m
Generative Models and LDA13m
Information Extraction18m
2 lecturas
Additional Resources & Readings10m
Post-Course Survey10m
2 ejercicios de práctica
Practice Quiz4m
Module 4 Quiz10m
32%
comenzó una nueva carrera después de completar estos cursos
34%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones
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.
Great course, very well balanced pace of learning. Adds good amount of working knowledge with NLP tools; definitely not covers everything but more than what I expected.
Instructor
V. G. Vinod Vydiswaran
Assistant ProfessorSchool of Information
Acerca de Universidad de Míchigan
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.
Acerca del programa especializado Ciencias de los Datos Aplicada con 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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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