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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/predictive-modeling-model-fitting-regression-analysis
课程评论：没有评论
课程名称：预测建模、模型拟合与回归分析 课程概述：欢迎参加《预测建模、模型拟合与回归分析》课程。本课程将探讨预测建模的不同方法，并讨论模型如何分为监督学习和无监督学习。我们将回顾如何对模型进行拟合、训练和评分，以便将其应用于历史数据和未来数据，以实现商业目标。此外，本课程还包括一个动手实践活动，以开发线性回归模型。 课程大纲： 模块1：预测建模 在本模块中，我们将比较预测分析与描述性分析，讨论两者所能获取的知识。我们还将探讨监督建模与无监督建模，这两种分析与机器学习的基础模型。 模块2：数据维度与分类分析 在本模块中，我们将探索数据分类的方法，以及如何利用决策树作为快速、易于使用且易于解释、说明和可视化的模型。 模块3：模型拟合 在本模块中，我们将探讨模型拟合的概念，以及创建一个能够拟合历史数据和未来数据的通用模型的终极目标。我们还会回顾如何对模型进行训练或评分，以便应用于新数据和未标记数据。 模块4：回归分析 在本模块中，我们将首先解释回归分析，这是一种数据科学专业人士常用的预测技术。我们还将讨论达到模型拟合并不保证模型能解决商业问题，以及即使是好的模型有时也可能导致不可行的结果。
Name:Predictive Modeling
Description:Welcome to Module 1, Predictive Modeling. In this module we will begin with a comparison of predictive and descriptive analytics, and discuss what can be learned from both. We will also discuss supervised and unsupervised modeling, two foundational models in analytics and machine learning.
Name:Data Dimensionality and Classification Analysis
Description:Welcome to Module 2, Data Dimensionality and Classification Analysis. In this module we will explore how data can be classified and how decision trees can be leveraged as a fast, easy to use a model that is easy to interpret, explain, and visualize.
Name:Model Fitting
Description:Welcome to Module 3, Model Fitting. In this module we will explore the concept of model fitting and how creating a generalized model that is able to fit both historical and future data is the ultimate goal. We will also review how a model can be trained or scored to apply to new and unlabeled data.
Name:Regression Analysis
Description:Welcome to Module 4, Regression Analysis. In this module we will begin with an explanation of regression analytics, a popular technique used by data science professionals to make predictions. We will also discuss how achieving model fit is not a guarantee that a model can help solve a business problem, and how even a good model can sometimes lead to unactionable outcomes.
Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.
