Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.
Este curso faz parte do Programa de cursos integrados Text Marketing Analytics
oferecido por
Programa de cursos integrados Text Marketing AnalyticsUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 3 no
Nível intermediário
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Aprox. 13 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
Describe the concept of topic modeling and related terminology (e.g., unsupervised machine learning)
Apply topic modeling to marketing data via a peer-graded project
Apply topic modeling to a variety of popular marketing use cases via homework assignments
Evaluate, tune and improve the performance the topic model you create for your project
Habilidades que você terá
- Topic Model
- Machine Learning
- Python Programming
- Unsupervised Text Classification
- Data Structure
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 3 no
Nível intermediário
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Aprox. 13 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade do Colorado em Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comece a trabalhar rumo ao seu mestrado
Este curso é parte da graduação 100% on-line Master of Science in Data Science da Universidade do Colorado em Boulder.
Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.
Programa - O que você aprenderá com este curso
4 horas para concluir
What is topic modeling?
In this module, we will cover the fundamental concepts of topic modeling, also known as unsupervised machine learning on unstructured text documents. We will contrast unsupervised methods to supervised ones and survey common applications of topic modeling.
4 horas para concluir
1 vídeo (Total 20 mín.), 3 leituras, 1 teste
4 horas para concluir
The Assumptions of a Topic Model, Bag of Words, and Natural Language Processing
In this module, we will go under the hood inside a topic modeling approach and understand what assumptions drive topic model fit. We will also uncover how bag-of-words approaches to topic modeling work, and the natural language processing required to produce meaningful topic modeling features.
4 horas para concluir
2 vídeos (Total 24 mín.), 1 leitura, 2 testes
1 hora para concluir
Prepping Amazon Review Data
In this module, we will cover how to parse through JSON-like data and segment it to create a corpus that is ready for the topic modeling process. We will cover how the data for your project is structured and its taxonomy.
1 hora para concluir
2 vídeos (Total 27 mín.), 2 leituras, 1 teste
2 horas para concluir
Pre-Processing Text and Training a Topic Model
In this module, we will take Amazon review data and load it into a corpus to preprocess it. We will cover how to build topic models from the data and also save those topic models.
2 horas para concluir
2 vídeos (Total 29 mín.), 2 leituras, 1 teste
Sobre Programa de cursos integrados Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also often involve relational and connected and involve networks. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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