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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/practical-machine-learning
课程评论：没有评论
课程名称：实用机器学习 概述：在数据科学家和数据分析师的工作中，预测和机器学习是最常见的任务之一。本课程将涵盖构建和应用预测函数的基本组件，重点关注实用应用。课程将提供训练集和测试集、过拟合和错误率等概念的基础知识。此外，课程还将介绍一系列基于模型和算法的机器学习方法，包括回归、分类树、朴素贝叶斯和随机森林。课程将涵盖构建预测函数的完整过程，包括数据收集、特征创建、算法应用和评估。 课程大纲： - 第一周：预测、错误与交叉验证 描述：本周将涵盖预测的定义、步骤的相对重要性、错误及交叉验证的概念。 - 第二周：Caret包 描述：本周将介绍caret包以及用于特征创建和预处理的工具。 - 第三周：使用树、随机森林与基于模型的预测 描述：本周我们将介绍多种机器学习算法，供您在课程项目中使用。 - 第四周：正则化回归及组合预测 描述：本周我们将讨论正则化回归和组合预测的方法。
Name:Week 1: Prediction, Errors, and Cross Validation
Description:This week will cover prediction, relative importance of steps, errors, and cross validation.
Name:Week 2: The Caret Package
Description:This week will introduce the caret package, tools for creating features and preprocessing.
Name:Week 3: Predicting with trees, Random Forests, & Model Based Predictions
Description:This week we introduce a number of machine learning algorithms you can use to complete your course project.
Name:Week 4: Regularized Regression and Combining Predictors
Description:This week, we will cover regularized regression and combining predictors.
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
