Machine Learning for Data Analysis

所在平台: Coursera

课程主页: https://www.coursera.org/learn/machine-learning-data-analysis

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课程简介

课程名称：数据分析的机器学习 概述：您是否对利用数据预测未来结果感兴趣？本课程将帮助您实现这一目标！机器学习是开发、测试和应用预测算法的过程。请确保在开始学习这些机器学习概念之前，先熟悉该专项课程的第三部分。本课程在第三部分的基础上，介绍许多其他机器学习的概念、技术和算法，从基本分类到决策树和聚类。通过完成本课程，您将学习如何应用、测试和解释机器学习算法，作为解决研究问题的替代方法。 课程大纲： 第一部分：决策树 描述：在这一节中，您将学习决策树，它是一种数据挖掘算法，可以从大量变量中选择那些对预测目标或响应变量最重要的变量及其相互作用。决策树通过反复应用一系列简单规则或标准，创建数据的分段或子组，选择最佳预测目标变量的变量组合。 第二部分：随机森林 描述：在这一节中，您将学习随机森林，这是一种数据挖掘算法，可以选择在众多变量中最重要的那些，以确定目标或响应变量。与决策树不同，随机森林的结果能够很好地泛化到新数据。 第三部分：套索回归 描述：套索回归分析是一种线性回归模型的收缩和变量选择方法。套索回归的目标是获得最小化定量响应变量预测误差的预测子集。通过对模型参数施加约束，套索回归会导致某些变量的回归系数向零收缩。收缩后回归系数为零的变量将从模型中排除，而非零回归系数的变量与响应变量关联最为显著。在这一节中，您将应用和解释套索回归分析，并通过使用k折交叉验证来选择最佳拟合模型，获得更准确的模型测试误差率估计。 第四部分：K均值聚类分析 描述：聚类分析是一种无监督机器学习方法，可以将数据集中观察结果分割为更小的一组聚类，其中每个观察结果仅属于一个聚类。聚类分析的目标是根据多个变量的相似响应，将观察结果分组或聚类。在这一节中，我们将向您展示如何使用K均值聚类分析来识别数据集中的观察聚类。您将获得通过图形方法解读聚类分析结果的经验，以帮助确定聚类的数量，并检查聚类变量的均值来评估聚类轮廓。最后，您将有机会通过检查未包含在聚类分析中的变量在聚类之间的差异来验证聚类解决方案。

课程大纲

Part: 1

Title:Decision Trees

Description:In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable.

Part: 2

Title:Random Forests

Description:In this session, you will learn about random forests, a type of data mining algorithm that can select from among a large number of variables those that are most important in determining the target or response variable to be explained. Unlike decision trees, the results of random forests generalize well to new data.

Part: 3

Title:Lasso Regression

Description:Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Explanatory variables can be either quantitative, categorical or both. In this session, you will apply and interpret a lasso regression analysis. You will also develop experience using k-fold cross validation to select the best fitting model and obtain a more accurate estimate of your model’s test error rate.

Part: 4

Title:K-Means Cluster Analysis

Description:Cluster analysis is an unsupervised machine learning method that partitions the observations in a data set into a smaller set of clusters where each observation belongs to only one cluster. The goal of cluster analysis is to group, or cluster, observations into subsets based on their similarity of responses on multiple variables. Clustering variables should be primarily quantitative variables, but binary variables may also be included. In this session, we will show you how to use k-means cluster analysis to identify clusters of observations in your data set. You will gain experience in interpreting cluster analysis results by using graphing methods to help you determine the number of clusters to interpret, and examining clustering variable means to evaluate the cluster profiles. Finally, you will get the opportunity to validate your cluster solution by examining differences between clusters on a variable not included in your cluster analysis.

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