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
oferecido por
Programa de cursos integrados Text Marketing AnalyticsUniversidade do Colorado em BoulderInformações sobre o curso
3.540 visualizações recentes
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 1 de 3 no
Nível 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
O 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 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 1 de 3 no
Nível 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
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
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 vídeos (Total 61 mín.), 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 vídeos (Total 39 mín.), 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 vídeos (Total 67 mín.), 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 vídeos (Total 17 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
O que recebo ao me inscrever nesta Especialização?
Existe algum auxílio financeiro disponível?
Mais dúvidas? Visite o Central de Ajuda ao estudante.
