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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/supervised-machine-learning-regression
课程评论：没有评论
课程名称：监督机器学习：回归 概述：本课程介绍了监督机器学习的一种主要建模类型：回归。您将学习如何训练回归模型来预测连续结果，以及如何使用误差指标来比较不同的模型。课程还将指导您了解最佳实践，包括训练和测试数据划分以及正则化技术。 课程目标： 通过本课程，您将能够： - 区分分类与回归在监督机器学习中的使用和应用。 - 描述并使用线性回归模型。 - 使用多种误差指标来比较和选择最适合您数据的线性回归模型。 - 阐述正则化如何帮助防止过拟合。 - 使用正则化回归技术：岭回归、LASSO和弹性网。 适合对象：本课程面向希望在商业环境中获得实际经验的有志数据科学家，特别是对监督机器学习回归技术感兴趣的人。 先修技能：为了充分利用本课程，您应该熟悉Python开发环境编程，以及具备数据清理、探索性数据分析、微积分、线性代数、概率和统计的基本理解。 课程大纲： 第一部分：监督机器学习与线性回归简介 描述：本模块简要概述监督机器学习及其主要应用：分类和回归。在介绍回归概念后，您将学习其最佳实践，以及如何测量误差和选择最适合您数据的回归模型。 第二部分：数据划分与交叉验证 描述：为了避免回归模型的过拟合，有一些最佳实践。其中之一是将数据划分为训练集和测试集。另一种选择是使用交叉验证。第三种选择是引入多项式特征。本模块将带您了解这些最佳实践的理论框架以及几个实践示例。 第三部分：带正则化技术的回归：岭回归、LASSO和弹性网 描述：本模块将向您讲解正则化回归的理论和一些实践示例，包括岭回归、LASSO和弹性网。您将了解到这些技术的主要优缺点，以及它们之间的异同。
Part: 1
Title:Introduction to Supervised Machine Learning and Linear Regression
Description:This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
Part: 2
Title:Data Splits and Cross Validation
Description:There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
Part: 3
Title:Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
Description:This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
