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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/supervised-text-classification-for-marketing-analytics
课程评论：没有评论
课程名称：监督文本分类与营销分析 课程概述：在营销数据分析中，数据的分类或标注至关重要。在当今大数据时代，营销数据的规模常常超出人力处理的能力。本课程将教学生如何运用监督深度学习来训练算法，以解决文本分类任务。课程通过Python的实际案例和教师主导的教程，带领学生逐步掌握这一概念。课程最后将有一个重要项目。 该课程可作为科罗拉多大学博尔德分校数据科学硕士（MS-DS）学位的一部分在Coursera平台上获得学分。MS-DS是一个跨学科的学位，汇集了来自应用数学、计算机科学、信息科学等多个部门的教师。该项目采用基于表现的招生方式，无需申请流程，适合具备计算机科学、信息科学、数学和统计等相关领域广泛教育背景和/或专业经验的个人。欲了解更多有关MS-DS项目的信息，请访问：https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 第一部分：监督机器学习工作流程 描述：学习不同类型的机器学习以及构建监督机器学习模型的操作步骤，并了解文本分类的性能指标。 第二部分：神经网络与深度学习 描述：学习神经网络及其在监督机器学习中的应用，深入真实的监督机器学习项目，并了解在开展项目时需要做出的关键决策。 第三部分：Google Colab与深度学习入门 描述：学习如何在Google Colab和Google Drive环境中工作，通过使用Google的Tensorflow和转换模型的封装来开始监督学习。 第四部分：线性模型与分类指标 描述：研讨基于线性模型的各种监督机器学习模型，并学习如何在scikit-learn中进行模型的外部性能分析。
Part: 1
Title:The Supervised Machine Learning Workflow
Description: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.
Part: 2
Title:Neural Networks and Deep Learning
Description: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.
Part: 3
Title:Getting Started with Google Colab and Deep Learning
Description: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.
Part: 4
Title:Linear Models and Classification Metrics
Description: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.
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. This course 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.
