Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised 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
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Você pode obter créditos de Universidade do Colorado em Boulder que contam para ganhar Master of Science in Data Science
Programa de cursos integrados Text Marketing AnalyticsUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
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Curso 1 de 3 no
NÃvel iniciante
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
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Experimente o Coursera for BusinessO que você vai aprender
Describe text classification and related terminology (e.g., supervised machine learning)
Apply text classification to marketing data through a peer-graded project
Apply text classification to a variety of popular marketing use cases via structured homeworks
Train, evaluate and improve the performance of the text classification models you create for your final project
Habilidades que você terá
- Assess Marketing Problems
- Supervised Learning Process
- Supervised Learning
- Classification Models
- Supervised Learning Outcomes
Prazos flexÃveis
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 1 de 3 no
NÃvel iniciante
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
Sua empresa se beneficiaria do treinamento dos funcionários em habilidades sob demanda?
Experimente o Coursera for Business
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 para obter o seu diploma
Este curso é parte de um programa de graduação on-line oferecido por Universidade do Colorado em Boulder. Quando você se inscreve através de uma universidade em um curso sem diploma, mas que conta com créditos e o completa on-line, ele conta como horas complementares na graduação da CU-Boulder. Você só precisa se inscrever pela universidade.
Programa - O que você aprenderá com este curso
8 horas para concluir
The Supervised Machine Learning Workflow
In this module, we will learn about the different types of machine learning that exist and the operational steps of building a supervised machine learning model. We will also cover performance metrics of text classification.
8 horas para concluir
4 vÃdeos (Total 61 mÃn.), 3 leituras, 2 testes
1 hora para concluir
Neural Networks and Deep Learning
In this module, we will learn about neural networks and supervised machine learning. Then we will dive into real supervised machine learning projects and the key decisions that need to be made when conducting one's own project.
1 hora para concluir
2 vÃdeos (Total 39 mÃn.), 2 leituras, 1 teste
2 horas para concluir
Getting Started with Google Colab and Deep Learning
In this module, we will learn how to work in the Google Colab and Google Drive environment. We will get started with supervised learning by using a wrapper for Google’s Tensorflow and transformer models.
2 horas para concluir
2 vÃdeos (Total 67 mÃn.), 2 leituras, 1 teste
1 hora para concluir
Linear Models and Classification Metrics
In this module, we will learn how to workshop a variety of supervised machine learning models that rely on linear-based models. We will also learn how to perform an external performance analysis of models in sci-kit learn.
1 hora para concluir
2 vÃdeos (Total 17 mÃn.), 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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