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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/meaningful-predictive-modeling
课程评论：没有评论
课程名称：有意义的预测建模 课程概述：本课程将帮助您评估和比较我们在之前课程中开发的模型。迄今为止，我们已经开发了回归和分类的技术，但如何确定分类器的误差应该多低（比如）才能说它是“足够好的”？或者我们应该如何判断两个回归算法中哪个更好？课程结束时，您将熟悉评估和比较分类器的诊断技术以及可用于不同回归和分类场景的性能度量。我们还将研究训练/验证/测试管道，以确保您开发的模型能够很好地泛化到新的（或“未见过的”）数据。 课程大纲： - 第一周：数据诊断 - 描述：在第一周，我们将介绍课程大纲，下载所有课程材料，并为课程配置系统。同时，我们将介绍监督学习结果的基本诊断知识。 - 第二周：代码库、正则化和模型评估 - 描述：这一周，我们将学习如何创建一个简单的词袋进行分析。还将讨论正则化及其在构建模型时的重要性。最后，我们将评估一个包含正则化的模型，重点关注分类器。 - 第三周：验证与管道 - 描述：这一周，我们将学习验证，并如何与训练和测试同时实施。我们还将介绍如何在Python中实施正则化管道，并提供一些最佳实践的指导。 - 期末项目： - 描述：在本课程的最后一周，您将继续构建第一和第二课程中关于预测分析的Python数据产品项目，使用简单的预测机器学习算法。找到一个数据集，对其进行清理，并对数据进行基本分析。评估您的模型，验证您的分析，并确保您没有对数据进行过拟合。
Name:Week 1: Diagnostics for Data
Description:For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.
Name:Week 2: Codebases, Regularization, and Evaluating a Model
Description:This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.
Name:Week 3: Validation and Pipelines
Description:This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.
Name:Final Project
Description:In the final week of this course, you will continue building on the project from the first and second courses of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better? By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.
